Bibliographic record
Abstract
In his important and careful attempt to reconstruct key aspects of the social experience of British unemployment over the last 200 years, the historian John Burnett has underlined what he sees as the remarkable degree of continuity marking the history of this phenomenon. ‘History is about continuity as well as change’, he writes, ‘and this study of unemployment illustrates some remarkable consistencies over time in public attitudes and personal responses despite the major economic and social transformations which have occurred during the last two centuries.’ This impression of continuity which tends to attach itself to the history of unemployment is only reinforced by our habit of thinking about unemployment in terms of numbers. Crime rates, poverty statistics, data about health, and, increasingly, information about markets – all regularly feature in the social and political life of nations. However, few of these series quite rival the impact or the cultural salience of the unemployment rate. While serious doubts about its accuracy have arisen in recent years, in part linked to concerns that it has been rather cynically manipulated for political reasons, the unemployment rate remains a key social indicator. Yet it is precisely this social perception that unemployment is, essentially, a number, a quantity, which makes it so familiar. The rate may go up or down, the distribution may change, but the thing remains essentially there.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.201 | 0.064 |
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".