‘Use-Value’ and the Re-thinking of Skills, Learning and the Labour Process
Bibliographic record
Abstract
Reviewing multiple traditions of social analysis of work, skill and knowledge this article seeks to renew the possibility for a critical, integrated approach. Contextualizing and then criticizing the ongoing ‘up-skilling/de-skilling impasse’, I offer discussion of several alternative conceptual resources that may contribute to a more robust appreciation for learning and human development, potentially unified under a suggested ‘Use-Value Thesis’ on the labour/learning process. It is argued that recognizing ‘use-value’ sets the stage for a broader systemic understanding of the contradictory processes (e.g. up-skilling/de-skilling, engagement/alienation, co-operation/conflict) that occur simultaneously in all workplaces under capitalism, and in turn offers a means to more coherently assess the full range of human learning.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.092 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".