Bibliographic record
Abstract
This chapter produces a socio-cultural critique of the ‘rational training’ workplace e-learning scenario. In this workplace e-learning scenario, workplace e-learning for workplace adult education training is used to justify the workforce through standards, categories, and measures. The alienating effects that arise out of this rush towards technocentric rationalization of the workforce through workplace e-learning are also discussed. These are the unintended and paradoxically opposite outcomes to the effects actually anticipated. An exploratory case study problematizes the unquestioned acceptance of the technological artefacts of workplace e-learning within organizations as credible sources to provide a rationale to justify workforces within workplaces. This approach critiques the presumption of infallibility of the technological artefacts of workplace e-learning; considers the short-comings of the conceiving of workplace e-learning as ‘finished’; and, reveals the ‘underdetermined’ nature of workplace e-learning technological artefacts. Socio-cultural insensitivity from workplace e-learning, in this scenario, comes from the basic, unquestioned assumption that workers are essentially socially flawed and culturally inferior; accountable for overcoming their sociocultural flaws and inferiorities; and, need to be justified by workplace e-learning, through standards, categories, and measures, to meet the expectations of the infallible and commodified workplace. A workplace e-learning that is deployed to justify the workforce, through standardization, categorization, and measurement, all result in a workforce being alienated from: (a) each other (worker-worker alienation); (b) their work (worker-work alienation); and, (c) their personal identities and sense of self (worker-identity alienation). Social rationalization is not the means to social justice in the workplace when it comes to workplace adult education and training, workplace e-learning, and the diverse and multicultural learning needs of a global cohort of adult learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".