Bibliographic record
Abstract
The neoliberal era has been one of increased inequality in both income and wealth distribution. The trend started in the 1970s in the US and has been most dramatic in the North Atlantic world but is also apparent in most of Europe. An OECD report (2017) noted that income inequality in Europe had grown over several decades. In Canada, gains in income were similarly concentrated at the top of the spectrum. Between 1982 and 2015, ‘The average real pre-tax income of the top 1 per cent of tax filers more than doubled, increasing by $320,000 – but the bottom 50 per cent of tax filers did not even keep up with inflation – their real income fell by an average of $1,546’ (Osberg, 2017: 28). More generally, various scholars have made the case that the neoliberal period has been characterized by the rapid growth of transnational firms and finance, and the ongoing transfer of income and wealth to the wealthy few (see Piketty, 2014; Atkinson, 2015; Peters, 2020). As Faroohar (2016: 15) summarizes, ‘the share of financiers within the top 1 per cent of the income distribution nearly doubled between 1979 and 2005’. By this calculation, an unprecedented level of inequality was already in place when the crisis struck. While income inequality earned through bonuses, super-salaries and a dismantling of progressive income tax systems is one part of the story, wealth (and finance) is particularly implicated because, again quoting Faroohar (2016: 15): [e]ven when you consider the salaries of the modern economy’s supermanagers – the CEOs, bankers, accountants, agents, consultants, and lawyers that groups like Occupy Wall Street rail against – it’s important to remember that somewhere between 30 and 80 percent of their income is awarded not in cash but in incentive stock options and stock shares.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".