The Limitations of Pieces of Paper: A Role for Social Science in Labour Law
Bibliographic record
Abstract
In this paper, the author argues that social science research should be given a more prominent role in formulating, applying, and evaluating labour laws. Unlike traditional legal research, which has a limited scope and tends to be based on rhetoric and the adversarial approach, social science research is characterized by systematic observation or experimentation intended to obtain positive knowledge or empirical evidence. It can therefore provide information that is more accurate and objective than the assumptions, inferences and untested beliefs on which the traditional approach is often founded (for example, those relating to the behaviour of the so-called normal or reasonable employee when employer unfair labour practices are alleged). The author also provides an introduction to the methods of social science research, both qualitative and quantitative, and illustrates the application of the latter to a labour law question (the effect of statutory ability to pay criteria on wage awards in interest arbitration). She notes, however, that social science research has limitations. In particular, it cannot be used to replace the normative or value judgments that inform decisions on the drafting or application of a law.
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 | 0.137 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.025 | 0.047 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".