Normalising standards in educational complexity: A network analysis
Bibliographic record
Abstract
The proliferation of transnational workplace sites has strengthened the demands for consistent standards of practice and operation. These are increasingly applied and regulated internationally through technologies such as ISO 9000. Workplace learning programs have been designed to reduce variation in skills and procedures at the local level, and to increase individuals’ compliance with regulatory manuals, audit forms, error reports etc. Yet at the same time, a key emphasis for organizations attempting to survive amidst global competition is to increase innovation across different units and different operation levels. This push for innovation has been coupled with ideals of a learning organization wherein all employees are supposed to learn continuously, e.g. to increase variation. This paper explores the organizational tension between centrally imposed demands for both standardized practice and innovative challenges to existing standards that often produces complete separation of design and execution functions, sometimes into sites located in different countries. It shows how in practice, workers continue to experiment and learn in ways that deliberately subvert reductionist standards measures, or that produce local innovations that are unrecognized by these measures.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".