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Almost three years after political scientist Rajni Kothari used the phrase Congress system in 1964 to describe the partys dominant position in Indian politics as an umbrella, an opposition party was grappling with another side of the story.

It was 1967. During a party meeting at Calicut, Deendayal Upadhyay, the leader of the BJPs predecessor Bharatiya Jan Sangh, talked about political untouchability practised against the party. He hinted at the unwillingness of other political forces to ally with the Jan Sangh in mounting a challenge to one-party dominance.

But five decades later, the turnaround has been historic. Today, the BJP forms the pivot of Indian politics a dominance that could not be foretold by even its spells of modest success and stints in power in some states and the centre from the 1990s.

This has also meant that recent years have seen a spurt in efforts to study, explain and interpret the partys phenomenal rise. More often than not, such efforts have been coloured by value judgements and the political outlook of the authors. Thus, they added little value to the task of understanding the political space, social dynamics, and evolving strategies that have shaped the partys ascendancy.

That, however, hasnt held back academic and journalist Nalin Mehtas The New BJP (published by Westland) from being the most ambitious attempt at using fresh data and analytical tools to dissect different facets of the current BJPs widening electoral appeal and social base, its policy measures and political messaging, especially the style and substance of its digital outreach. In that context, the book marks a point of departure in treatises on BJP a genre that has come into its own in recent years.

At the outset, while steering clear of the lure to evaluate recent strands and shifts in Indian politics, Mehta is clear about what the book will probe.

Why did the BJP start winning on this scale? he writes. Was it only because of a cultural shift in India? Or was its edifice of expansion built on creating a much wider, deeper superstructure of new constituencies of voters who aligned with it for reasons beyond the cultural divide? How did the BJPs growth patterns differ across Indias regions, in new catchment areas where it had never held sway before or in areas where it failed to make inroads? In other words, what really was happening in India, how did the BJP systematically become the countrys largest political party and its fulcrum of power? These are the fundamental questions I seek to answer.

Such questions, as he sees it, need to be examined because it was vital at this point in Indias national journey to understand the levers of its [the BJPs] political growth dispassionately.

Its anybodys guess as to whether all these questions, that too in their entirety, could find answers in the book. But the empirical goals it has set for itself are impressive, even if partly achieved. Its methodological core field visits, interviews and conversations over the years, written sources, archives, documents has led to two innovative statistical measures.

First, Mehta and data scientist Rishabh Srivastava developed a data-mining software called Normative Analysis of Reporting and Discourse, or NARAD. This was used to create an original database of 11,588 BJP-related documents between 2006 and 2019, approximating 17.9 million words. AI tools were put to use to identify patterns in such a wide array of data.

The inferences that could be drawn are important. On political messaging, for instance, unlike what many commentators believe, religious identity issues like the Ayodhya temple featured quite low in the partys pecking order of communication priorities, coming after issues like development, defence, agriculture, women, youth and Kashmir. Interestingly, a degree of the relentless targeting of the key national opposition was also visible as the data revealed that the BJP used more words slamming the Congress than talking about itself.

Second, Mehta, in collaboration with journalist Sanjeev Singh, came up with the Mehta-Singh index to measure the BJPs social base. The fact that the party worked on the social engineering of expanding the party beyond the limits of upper caste support groups to a number of OBC and Dalit groups is documented through numbers. The data showed that while becoming a poll behemoth, the BJP ensured that it became more and more socially representative. In the Hindi heartland states, this has also been used to challenge the social coalition of regional parties. On the national stage, the BJP was alert to take into its fold the OBC support base in a number of states, a catchment area left open by the Congress in the early decades after independence. In the pursuit of what American political scientist Paul Brass had termed as a coalition of extremes in states like Bihar which meant a support base among upper castes, Muslims and Dalits the Congress left a large section of OBCs unattended and unaccommodated.

Even in the recent Uttar Pradesh poll, the widening of the partys support base groups, as well as diversity in its representation, further attest to the books statistical indicators. On this count, Mehta could have elaborated on how the partys successful forays into winning new caste groups of OBCs and Dalits was accomplished without distancing its traditional voter base among upper caste groups. Here, the book seems more focused on what and not why an understandable frame but perhaps putting together both could have further benefited students of social politics.

The other baggage that the BJP has been able to shed in recent years has been its earlier limitation as an urban party. In Hindi heartland states, and Mehta uses UP as a large case study to produce data, the party has emerged as the rural party as well, establishing a clear edge over its rivals. Overcoming this spatial challenge was important for the partys growth. As early as 1990, scholar Bruce Desmond Grahams study of the BJS (Hindu Nationalism and Indian Politics: The Origins and the Development of Bharatiya Jana Sangha, Cambridge University Press, 1990), the BJPs precursor, had noted that in states like UP, the partys efforts to expand in rural areas were not successful, and were even resisted. Put in that context, the BJPs success today in making deep inroads into villages and then building rural strongholds in the Hindi heartland seems a feat accomplished against the weight of history.

A good measure of this turnaround has also been achieved by how the BJP has prioritised social welfarism in its policy interventions. Various schemes centred around cash transfers, access to cooking gas, healthcare, housing and efficient delivery of rations, to name a few, have created a new political constituency of beneficiaries, called the labharthi vote. More recently, even the rough edges of poor health infrastructure during the pandemic were, to an extent, blunted by taming extreme hardship through welfare initiatives. The book puts together numerous sets of data on welfare interventions and the focus on delivery. Even if this could be seen as taking a leaf out of the Congresss stylebook, its execution and public projection has built a support base of its own, especially among women voters.

However, its not only welfare politics that has brought it the goodwill of women voters. In Mehtas analytical frame, the BJPs casting the idea of the national in the image of motherhood had a ring of reaching out to a different set of spiritually-inclined womanhood. The author reflects on how it had the imprint of the milieu-specific agency of womanhood. That the third wave feminism could allow for these variations might be another argument that the book fleetingly touches upon.That, however, is a more abstract part of an effort that has more to do with hard numbers of empirical analysis.

The partys successful expansion in northeast states, and its challenges and mixed results (such as in Karnataka) in the south, also finds space. As a party working to make inroads into new geography and support groups, the BJPs failures and its review are seen as vital to how the party holds onto its ambition of a pan-India footprint, even sway.

In navigating the numbers, Mehta is careful about not getting lost in them. The book is sprinkled with notes from field studies and a number of conversations. Some of which could have been given more space to put the numbers in perspective, such as how close or sometimes misleading they could be in measuring mass perceptions. To add to that, the book makes interesting points about the BJP-RSS convergence and divergence. In the process, the book also counters the conventional thinking about the BJPs dependence on RSS workers for campaigns. In the last few years, the BJP cadre strength has outnumbered that of the RSS by a fair distance, thus making the latter an auxiliary force to the mainstay of the BJP workforce in conducting poll campaigns or public communication at the ground level.

The 809-page tome has obviously covered more ground, including chapters on the partys ideological evolution, particularly the economic worldview. Similarly, it has traced some parts of the current discourse in general and the partys perspectives to the early decades of post-independence politics in India. Even though leaving a historical register isnt one of the books aims, it has left some informative notes on party competition.

Amid the recent flurry of party-specific treatises, Nalin Mehta has offered a work of empirical ambition, with a blend of data-rich insight, the analytical frame of a political scientist, and journalistic reportage. The book will provide students of politics some of the tools to revisit the BJP turnaround story and its rise to the pivot of Indian polity and reshaping social coalitions. In doing so, the focus of what might at times make the why take a backseat, but that would have interrupted its empirical narrative. Answering the many whys, and there could be many answers, awaits another tome. By all accounts, the genre of treatises on the party is going to have a longer run.

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From rich data to dispassionate analysis, Nalin Mehtas The New BJP is a work of empirical ambition - Newslaundry

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Canadian mining has long struggled to enhance diversity at the board and senior leadership levels. But while the industry has consistently ranked near the bottom among industries in terms of the number and percentage of women in senior leadership roles, change is happening and a number of companies in the industry are taking steps to be more inclusive.

Lucara Diamond is one such company. In addition to being a mining company, Lucara is also a tech company. Its proprietary diamond sales platform, called Clara, is built on blockchain technology that uses computer algorithms to match its diamond production to buyer preferences.

The link between the diamonds and the technology is Lucaras president and CEO Eira Thomas. Thomas, a geologist and 25-year industry veteran, co-founded Lucara in 2008 and, a decade later, was developing Clara when Lucaras board recruited her to replace retiring CEO William Lamb and bought Clara in the process.

Lucara also stands out among mining companies because Thomas is a woman and three of its top six executives are female. In addition, three directors of Lucaras seven-memberboard are women. Thomas isnt the only woman CEO in Canadian mining, but they are nearly as rare as diamonds like the Sewel (one of the exceptional large diamonds mined at Lucaras Karowe mine in Botswana). According to data compiled for Oslers2021 Diversity Disclosure Practicesreport,of the Canadian public mining companies included in the data set,there are only six other CEOswho are women.

Canadas mining sectoris making modest gains in the number and percentage of female directors and women in senior executive positions (particularly on mining boards),but obstacles remain.

Attracting women to careers in mining and keeping them once they are there has long been an issue. The scope of this problem was highlighted in a 2016 industry action plan from Women in Mining Canada. It cited Mining Industry Human Resources Council (MiHR) figures that showed mining has a lower representation of women compared to the very same occupation in other industriesThis holds true whether the occupation is traditionally associated with a higheror lower representation of women.

These challenges might also help to explain, at least in part, why career profiles of women mining executives and directors suggest a significant proportion enter the field in senior roles directly from other professions, such as accounting, law or finance. Placements for these women tend to be among the mid-size and larger firms, where there is more of a focus on governance and a diverse matrix of skillsets among directors. The makeup of most junior mining boards, by comparison, skews more heavily to a tighter circle of geologists and mining engineers fields where women tend to be underrepresented.

The disparity in diversity levels between the boards of Canadian mining companies and other TSX-listed company boards is significant. Data compiled for Oslers2021 Diversity Disclosure Practicesreport shows that just 19% of directors at mining companies in 2021 were women (up from 18% in 2020), compared to 23% for TSX-listed companies as a whole and 33% at S&P/TSX 60 companies. On a per-board basis, the number of women directors was 1.45, versus 1.83 per board for TSX-listed companies overall and 3.72 for S&P/TSX 60 companies.

Although significant, there is less of a differential between the percentage of women executive officers in mining (15%) compared to TSX-listed companies (18%) and S&P/TSX 60 companies (22%).

Oslers survey data for directors does indicate a substantial overall improvement since 2015, the first full year after the diversity disclosure requirement was introduced. That year, only 7% of directors at mining companies were women. The rate of increase to 19% compares favourably to the improvement rate for all TSX-listed companies, where percentage representation rose to 23% in 2021 from 10% in 2015.

The story for women executive officers in the mining sector is also consistent with the trend for TSX-listed companies as a whole. Since 2015, change has been slow and representation remains low in absolute terms (11% in 2015, and 15% in 2021).

The story with respect to the representation of other diverse groups, including Indigenous peoples and visible minorities, is a mixed bag.

According to MiHRs2019 Canadian Mining Labour Market Outlook, the representation of Indigenous peoples in the workforce is substantially higher in Canadian mining (7%) compared to other industries (4%), and higher than their representation in Canadas population (5%). Data from Oslers2021 Diversity Disclosure Practicesreport found that among federally-incorporated public companies 0.5% of the board positions and virtually no executive officer positions were held by Indigenous peoples. However, although the absolute number of board seats held by Indigenous peoples overall is clearly very low, five of the eight such seats among disclosing companies were on the boards of mining companies. In common with other federally-incorporated public companies, mining companies had virtually no executive officers who are Indigenous.

The2019 Canadian Mining Labour Market Outlookreported that the representation of visible minorities in the workforce is substantially lower in Canadian mining (9%) compared to other industries (21%), and their representation in Canadas population (22%). Data from Oslers report found that among federally-incorporated public companies, 6.9% of the board positions were held by visible minorities and about 30% of such companies reported having at least one executive officer who is a visible minority. Among mining companies providing disclosure, the percentage for directors was lower at approximately 2.5%, while mining companies had only 14 executive officers who are visible minorities.

Women in Mining Canadas action plan offers a twofold prescription to boost female representation: Outward-focused action to fill the talent pipeline [and]inward-focused action that changes workplace cultures.

Oslers research has identified a number of Canadian mining companies that are consistent leaders in best practices to support such efforts. These include:

>Cameco diversity and inclusion training, featuring extensive online resources and other awareness initiatives via posters and displays

>Teck Resources promoting culture change with gender-neutral approach to job titles and descriptions, as well as family-friendly policies for mid-career women

>Kinross Gold strategic implementation of global written inclusion and diversity guidelines promoting a more inclusive workplace

>Lundin Mining has established a 14-person multi-disciplinary working group to further the corporations diversity and inclusion agenda. Resources and forums are being made available to enable uncomfortable conversations. The corporation has committed to continually evaluating the effectiveness of this program and to modify its approach to ensure it is keeping up with best practices.

>New Gold participates in the International Women in Mining mentoring program that provides mentoring opportunities for female staff across the organization and also provides female corporate employees with membership in Women in Mining Toronto.

>OceanaGoldadopted a monitoring program to track progress, and a project to identify and correct gender pay equity gaps.

Anumber of other initiatives have been launched in the Canadian mining industry in recent years to address the underrepresentation of women and encourage further progress in achieving gender parity. For example, MiHR established the Gender Equity in Mining Works (GEM Works) program that trains gender champions and change agents within organizations with a view to eliminating unintentional barriers to gender inclusion. The Mining Association of Canadas annual Women in Mining newsletter aims to raise the profile of women by highlighting the work being done by women in the sector. And organizations like Women in Mining Canada and Women Who Rock focus on mentorship and encouraging initiatives that promote professional development for women.

With respect to other diverse groups, Oslers research also identified some Canadian mining companies that are taking action to enhance Indigenous representation. For example, before it merged with Agnico Eagle, Kirkland Lake Gold had implemented cross-cultural training at its sites hoping to recruit more effectively and to build trusted and prosperous partnerships with Indigenous communities.

Looking ahead, the key to greater progress for Canadian mining companies overall looks to be replicating the gender practices and diversity performance of our largest miners at the mid-tier and junior levels and replicating them for the benefit of other underrepresented groupsat all levels.

Andrew MacDougall and John M. Valley are both partners, corporate at Osler.

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Miners lag on diversity in leadership: Osler study finds more progress for women at the board and senior levels than Indigenous people and visible...

In line with this, the invasion of Ukraine by Russia has also led to increased cyberthreat fears, notably following an issue from the Cybersecurity and Infrastructure Security Agency (CISA) of a Shields Up” cyber warning. The agency said that while there were no specific threats linked to the US or North America, organizations should still be prepared to respond to any kind of cyber activity.

With that in mind, companies like Datametrex AI Ltd. (TSXV:DM, OTCQB:DTMXF, Forum) are innovators in the cybersecurity space thanks to its artificial intelligence (AI) and machine learning (ML)-based technology that can identify threats by scanning conversations on social media.

With a seven-year history, Datametrex has been working in the cybersecurity field with organizations such as NATO, the Canadian government and the United States Armed Forces for the six years to develop and deploy AI and ML solutions.

In line with this, in an interview with Stockhouse Editorial, Datametrex CEO Marshall Gunter said the company has taken battle-tested military-grade software and moved it into the private sector. He added that the company is now providing those same solutions to companies like Samsung and Lotte Corporation.

Everyone needs cybersecurity,” Gunter told Stockhouse Editorial. Right now, in this day and age, we are producing levels of data that have never been seen before.”

As people’s lives become more increasingly online, companies like Datametrex are becoming more integrated than ever thanks to its solutions that identify threats and help stop the spread of misinformation.

Identifying threats through AI

Gunter explained that the company’s AI software solutions take advantage and use the latest AI and ML techniques to try and find unknown unknowns.”

These are the things that you don’t really know about but could bite you in the days to come,” he said.

Gunter said that the company’s solutions can identify the words and people who write them through social media platforms in order to reveal the unknown unknowns,’ which are considered risks, which then enables organizations to make informed decisions.

[Datametrex] is able to find those things [organizations] should be aware of today but will become a problem in two or three days,” he said, adding that they are threats that should be looked at sooner before they become problems.

Notably, the company’s subsidiary, Nexalogy is an AI-based social data mining and analysis entity that searches social media in order to identify potential threats The deep analytics platform collects and analyses information from social media platforms such as Twitter, Facebook, Tumblr, blog forums, online news sites, Google Alerts, blogs and RSS feeds, which then allows organizations to make more informed decisions by pinpointing trends and risks, among other things.

(Click to enlarge image)

Gunter explained that while the technology scans information, it does so with privacy in mind and that Datametrex overall takes privacy very, very seriously.” He said the company complies with all applicable laws and suggestions from the governing bodies the company is involved with.

[Datametrex is] compliant with all levels, all the way up to the federal levels in the US and Canada and every jurisdiction that we work in,” he said. Gunter reiterated that the company’s solutions do not analyze any private information or information that isn’t available to the general public.

Tackling the spread of disinformation

In today’s digital world, the spread of disinformation is more rapid than ever. In a 2019 report, it is estimated that online fake news is costing roughly US$78 billion globally each year.

Fake news, or disinformation, can originate and can spread from anywhere and be shared across a wide range of platforms. This can include deep fakes, biased reporting and selectively quoting sources, which can make it challenging for consumers to determine the truth.

In the wake of Russia’s invasion of Ukraine, the spread of disinformation has been rampant as edited and manipulated information has been surging in online forums and news outlets out of Russia.

Companies like Datametrex are working to solve these problems case in point, Gunter said that the company has been working in the field of misinformation for many years.

[Datametrex has been] on a task force with NATO and [the company] still participates in the task force to fight misinformation,” he said. [Datametrex] has developed techniques and software that does help fight misinformation in conjunction with defense research in Canada, the Canadian Armed Forces, NATO, the US Airforce, etc.”

Notably, the company, through its subsidiary Nexalogy Environics, developed a Fake News filter in 2018 called the Next Intelligence, which includes automated detection of suspicious news sources on social media.

In 2020, the company worked with the US government to develop strategies on preventing the spread of misinformation on COVID-19. Just before that, Datametrex completed work for Democracy Labs on disinformation in social media regarding the pandemic and secured a relationship with Carnegie Mellon University IDeaS.

[Datametrex] has developed tools and techniques that help shed light on misinformation and bring that to the attention of everyone involved so that [the company] can expose it and show the danger of fake news to the public,” Gunter said.

He explained that in addition to working with public entities such as governments, Datametrex also works with the private sector, including organizations such as Democracy Labs to prevent the surface of misinformation.

The Datametrex AI advantage

While cybersecurity solutions aren’t new, Datametrex AI has a unique advantage when compared to its peers in the space.

Gunter explained that there are three things that differentiate Datametrex from its peers in the space, including:

Leadership team

Marshall Gunter, CEO

Marshall Gunter has an engineering background that is rooted in big data analysis and machine learning at scale. Gunter has worked with Sequoia Capital and Lightspeed Venture Partners where he was responsible for bringing Varagesale to the mass market.

Gunter led Varagesale from a one room shop to an engineering department of over 60 people and played a leading role in its $35 million raise. Gunter’s other experience also includes leading the team that built iSentium’s sentiment engine, which used a patented natural Language Processing system to extract sentiment from unstructured social content that is transformed into highly actionable indicators in finance, brand management and politics.

Don Shim, CPA, CA CFO

Don Shim has had an accounting and finance career in both the US and Canada, having worked and audited publicly traded junior mining companies and high-tech industries. Shim is a member of the Chartered Professional Accounts of British Columbia and a Certified Public Accountant registered in Illinois.

Shim has also been an audit partner on a range of audit engagements for various junior mining companies, oil and gas, pharmaceuticals and high-tech. Shim has also assisted start-up companies going public on the TSX Venture Exchange, Canadian Securities Exchange and the OTC Market. Shim also teaches accounting at a local Vancouver, BC college and acts as a facilitator at CPA Western School of Business.

Dr. Omar Sharif, Chief Medical Officer

Dr. Omar Sharif received his training from the University of Manitoba and then took a range of leading roles within his departments of hospital medicines and emergency medicine in BC and Alberta. Having worked for over 10 years in these fields, Sharif gained status as an instructor at the University of British Columbia.

The investment opportunity

With a market capitalization of C$55.05 million, Datametrex currently has a share price of $0.155 and trades on the TSX Venture Exchange in , but will be uplisted to the TSX by Q3 of this year. At present, Datametrex has 355.17 million shares and 356.17 million shares outstanding.

Thanks to the company’s proven track record and relationships with government entities, the US Airforce, Canadian Armed Forces and other defence agencies, Datametrex has been able to prove that its technology has the legs to stand on,

What’s more, Datametrex is tackling a problem that no other company is currently doing revealing the unknown unknowns’ which means not only is the company ahead of the curve but has the skills to develop a solution that hasn’t been done before.

Additionally, as the company continues to sign new , notably two major AI government contracts totalling over $42 million CAD and as AI and solutions become more pressing companies like Datametrex are solid investment opportunities now, and in the long haul.

For more information, visit Datametrex.

FULL DISCLOSURE: This is a paid article produced by Stockhouse Publishing.

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The Software Stopping Misinformation on Russia's War in Ukraine - Stockhouse

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There are two forms of data analysis that can be used for extracting models describing important classes or to predict future data trends. These two forms are as follows

Classification models predict categorical class labels; and prediction models predict continuous valued functions. For example, we can build a classification model to categorize bank loan applications as either safe or risky, or a prediction model to predict the expenditures in dollars of potential customers on computer equipment given their income and occupation.

Following are the examples of cases where the data analysis task is Classification

A bank loan officer wants to analyze the data in order to know which customer (loan applicant) are risky or which are safe.

A marketing manager at a company needs to analyze a customer with a given profile, who will buy a new computer.

In both of the above examples, a model or classifier is constructed to predict the categorical labels. These labels are risky or safe for loan application data and yes or no for marketing data.

Following are the examples of cases where the data analysis task is Prediction

Suppose the marketing manager needs to predict how much a given customer will spend during a sale at his company. In this example we are bothered to predict a numeric value. Therefore the data analysis task is an example of numeric prediction. In this case, a model or a predictor will be constructed that predicts a continuous-valued-function or ordered value.

Note Regression analysis is a statistical methodology that is most often used for numeric prediction.

With the help of the bank loan application that we have discussed above, let us understand the working of classification. The Data Classification process includes two steps

This step is the learning step or the learning phase.

In this step the classification algorithms build the classifier.

The classifier is built from the training set made up of database tuples and their associated class labels.

Each tuple that constitutes the training set is referred to as a category or class. These tuples can also be referred to as sample, object or data points.

In this step, the classifier is used for classification. Here the test data is used to estimate the accuracy of classification rules. The classification rules can be applied to the new data tuples if the accuracy is considered acceptable.

The major issue is preparing the data for Classification and Prediction. Preparing the data involves the following activities

Data Cleaning Data cleaning involves removing the noise and treatment of missing values. The noise is removed by applying smoothing techniques and the problem of missing values is solved by replacing a missing value with most commonly occurring value for that attribute.

Relevance Analysis Database may also have the irrelevant attributes. Correlation analysis is used to know whether any two given attributes are related.

Data Transformation and reduction The data can be transformed by any of the following methods.

Normalization The data is transformed using normalization. Normalization involves scaling all values for given attribute in order to make them fall within a small specified range. Normalization is used when in the learning step, the neural networks or the methods involving measurements are used.

Generalization The data can also be transformed by generalizing it to the higher concept. For this purpose we can use the concept hierarchies.

Note Data can also be reduced by some other methods such as wavelet transformation, binning, histogram analysis, and clustering.

Here is the criteria for comparing the methods of Classification and Prediction

Accuracy Accuracy of classifier refers to the ability of classifier. It predict the class label correctly and the accuracy of the predictor refers to how well a given predictor can guess the value of predicted attribute for a new data.

Speed This refers to the computational cost in generating and using the classifier or predictor.

Robustness It refers to the ability of classifier or predictor to make correct predictions from given noisy data.

Scalability Scalability refers to the ability to construct the classifier or predictor efficiently; given large amount of data.

Interpretability It refers to what extent the classifier or predictor understands.

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Data Mining - Classification & Prediction