Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it