Corrosive Solo Self-Employment: The Qualitative and Quantitative Impact of Neo-Villeiny in the Fitness Industry
Bibliographic record
Abstract
Solo self-employment (SSE) is a heterogeneous category that requires further contextual analysis across different occupations and professions. Drawing on a multiple methods study, this article reveals the corrosive impact of a specific type of SSE, that of the self-employed personal trainer (SEPT) in the UK fitness industry. SEPTs pay rent in cash and in kind and carry out (unpaid) tasks that would otherwise be undertaken by contracted employees leading to a corrosive impact on employment. Despite a dramatic increase in gym membership in the UK between 2006 and 2021, Office for National Statistics data reveal a decrease in employment in both proximal fitness industry functions and for personal trainers, alongside an increase in self-employed fitness instructors of more than 400%. Our study shows that the nature of the work of this growing SEPT workforce has a direct and negative (corrosive) impact on those undertaking the work.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".