Bibliographic record
Abstract
Industry-wide bargaining to be suspended, €50 billion to be raised through privatization, social security to be cut by more than €4 billion over four years, nominal public sector wages to be slashed by 20 per cent, and on it went. Such was the list, so named were the targets. It was 2011, the Eurozone was in chaos, the global economy was in tatters, and the stimulus era proved fleeting. Austerity was widely en vogue and it was being visited in dramatic fashion on Greece: the Troika bailout demanded it, capitalist interests needed it, and the government and its people were put on notice (BBC, 2011). Greece is an exceptional case, but it is far from an isolated one. The global financial crisis of 2008, the ensuing and prolonged economic crisis, and policies of austerity implemented from 2010 have imposed major costs on most Western societies. These include direct economic costs such as lower GDP, slower economic growth, higher unemployment and lost output, various forms of underemployment, much of it in precarious and poorly paid jobs, and increased household debt obligations that drag down disposable income. Other, perhaps less direct, effects can be categorized as social and human costs. Phenomena such as inequality (a legacy of the entire neoliberal period: see Piketty, 2014; Atkinson, 2015) increased in the post-crisis years (Schneider et al, 2017), and higher unemployment and insecurity were exacerbated by austerity measures such as cuts in social and health care spending, and labour market restructuring. Inequality and unemployment are linked to various social problems involving mental health, drug use and addiction, lower life expectancy, increased obesity, low education achievement and aspirations, more violence and less social mobility (Wilkinson and Pickett, 2009, chapters 4–12). The human and social costs are significant; and often compounded by divisions of gender, race, migration status and age (on the gendered effects of the global financial crisis, see Hozic and True, 2016, part I). For youth in Ireland and Spain, for example, the damage to their employment and economic prospects was so severe that talk of a ‘lost generation’ became commonplace.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.014 |
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".