Bibliographic record
Abstract
This book exposes how inequalities based on class and social background arise from employment practices in the digital age; where the infiltration of personal and family life through social media weighs on who is hired and fired; where platforms can be used to hide job advertisements from people who live in certain parts of a city; where algorithms assess socio-economic data to filter candidates; where human interviewers are replaced by artificial intelligence with design that disadvantages candidates who use certain classed language; and where already vulnerable groups are disadvantaged by gamified recruitment, exploited by the technology driven gig economy or become victims of the post-pandemic shift to remote working. The extent to which or whether these inequalities create risks of discrimination based on certain protected attributes is examined, including ‘social origin’ in international labour law and the laws of Australia and South Africa, ‘social condition’ and ‘family status’ in laws within Canada, ‘family status’ in New Zealand law, and others. The analysis in this book reveals deficiencies in the ability of the law to address these inequalities, and thus makes proposals for law reform and the development of workplace policy which may help to disassemble the structural barriers which are being constructed in the digital age.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".