Bibliographic record
Abstract
This chapter discusses social change as context and social responsibility as impetus for a socioculturally sensitive research and study of workplace e-learning. Current interventions of workplace elearning, when not accompanied by socio-cultural sensitivity, are destined to falter with respect to adequate workplace adult education and training for a global, diverse workforce. To describe transformation and change happening at work as ‘phenomena’ is an understatement. Workplace transformations and workforce changes are, quite literally, daily events. A dynamic and global workforce lives and works in the global age. Workers now participate in organizations comprising of people who are more experientially and demographically diverse. Consumer tastes and loyalties are incessantly transient. Organizations are also morphing with respect to technologies, processes, jobs, and accountabilities. Against this backdrop, the growing reliance on workplace e-learning as a complete solution based on the goals of cost savings and process efficiencies is increasingly problematic. The assumption of the ubiquity of the technological or financial artefacts of hardware and software, as sufficient to overcome diverse workforce learning needs fundamentally naive. The importance and necessity of broaching workplace e-learning as a socio-culturally negotiated idea, and not as just a technological or financial artefact, now becomes clearer. Social change does impact workplace transformations and workforce changes, which in turn directly influence workplace e-learning outcomes. Social responsibility now also becomes an ‘impetus’ for the socio-cultural sensitivity of workplace e-learning and the benefit of a global and diverse cohort of adult learners contending with workplace transformations, workforce changes, and social changes.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".