Bibliographic record
Abstract
This chapter concerns many of the challenges facing socio-cultural researchers of workplace e-learning when attempting a social critique of workplace elearning. These obstacles include finding a common ground to begin a socio-culturally based research and study of workplace e-learning as well as using an approach that authentically balances ‘distance’ and ‘education’ so that distance education does not become a ‘distant education’. The overwhelming emphasis on the technological artefacts of workplace e-learning are not having the expected impacts on workplace adult education and training to the degree so profoundly anticipated by so many. The research and study of workplace e-learning as a socio-culturally negotiated ‘idea’ may be one such way. To do this, notions of social theory, taxonomy, and the researcher, as they relate to the field of adult education, and for a global workforce of adult learners, now become necessary. The complexity of approaching the diverse field of adult education with respect to social theory is explained, as are some of the challenges of applying the socio-cultural sensitivity taxonomy by using adult education as a backdrop for understanding workplace e-learning. ‘Socio-cultural Sensitivity Taxonomy for Workplace E-learning’ is presented and comprises four basic elements: (a) a context (social change) and an impetus (social responsibility) for a socio-culturally based research and study of workplace e-learning; (b) two outcomes (normalization and universalization) of technological artefactual approaches to workplace e-learning research and study; (c) two dominant cultural paradigms (commodified knowledges and innovative artefact) shaping workplace e-learning historicity in organizations; and, (d) four workplace e-learning scenarios (instrumental instruction, rational training, dehumanizing ideologies, and social integration), that all present socio-cultural impacts for the workforce from socio-culturally insensitive, technological artefactual approaches to workplace e-learning research and study. Figure 1 and Figure 2, originally from the Preface, are re-presented here, more formally.
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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 | 0.065 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.115 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.005 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".