Frontline Workers and the Role of Legal and Regulatory Intermediaries (LIEPP Working Paper, n°94)
Bibliographic record
Abstract
The paper deals with legal intermediaries, as two streams of research apprehend and define them in recent and dynamic works. One, rooted in political science, studies regulatory intermediaries (LeviFaur et al., 2017; Bes, 2019), as actors between regulators and regulated, whereas the other, rooted in the Law and Society field and sociology, analyses legal intermediaries (Edelman, 2016; Talesh and Pélisse, 2019 ; Billows and alii 2019), as a broader and more bottom up category describing actors handling and dealing with legal rules even if they are not legal professionals. The article reviews these two approaches, showing their proximity but also differences and evoking empirical examples of these legal intermediaries like managers and union activists in companies, safety officers or job counsellors in private or public organizations. The paper then advances the need to study frontline workers with whom legal intermediaries interact in organizations, to understand how regulations and rules are implemented and influence social and economic practices in organizations. It finally shows how frontline workers are increasingly being called upon to become legal intermediaries themselves, not without consequences on the increased accountability expected from them.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 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".