Bibliographic record
Abstract
Introduction This book has two aspects and aims. First, it aims to unravel the extent to which discrimination in employment based on class and factors reflective of social background is prohibited in Australia, South Africa, Canada and New Zealand, and key differences in the law of each of these jurisdictions. Second, it examines the application of the law to the use of new technology and practices, to expose how their use creates risks of this type of discrimination and to propose how these technologies and practices can be re-imagined to reduce these risks. The first of the book's aims are achieved in Chapters 2 and 3. Chapter 2 considers whether, and the extent to which, discrimination based on class and factors reflective of social background is prohibited in international labour law as part of the prohibition on ‘social origin’ discrimination in conventions of the International Labour Organisation (‘ILO’). Chapter 3 then maps the legal frameworks in four common law countries (Australia, South Africa, Canada and New Zealand) to unravel the extent to which discrimination in employment based on class and factors reflective of social background is prohibited in these countries. The analysis in these chapters reveal that whilst ‘class’ and ‘social background’ are not expressly listed as grounds of discrimination in legislation within these jurisdictions, certain listed grounds are understood in terms of class and/or factors reflective of social background. This includes the grounds of ‘social origin’ in Australian and South African law, ‘social condition’ and ‘family status’ in laws within Canada, and ‘family status’ in New Zealand law, amongst other grounds. The second of the book's aims are achieved in Chapters 4, 5, 6 and 7. Chapter 4 examines an employer's use of social media, such as for cybervetting, job advertisement targeting, and terminating an employee's employment for social media posts. Chapter 5 examines automated candidate screening technologies, such as the use of certain algorithms and AI in recruitment. Chapter 6 examines the changing nature of work in the digital age, including certain aspects of platform work in the ‘gig economy’ and the post-pandemic shift to remote working and homeworking. Chapters 4, 5 and 6 expose how all these practices create significant inequalities and opportunities for discrimination based on class and/or certain factors reflective of social background.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".