Bibliographic record
Abstract
The global, knowledge-based economy is causing rapid change when it comes to workforce composition and the nature and character of work itself. At the same time, ‘e-learning’ is increasingly positioned as the panacea for workplace learning needs for a transforming workplace and the global, knowledge-based economy (Industry Canada, 2005; Rohrbach, 2007). In this information age of intense political, social, technological, and environmental upheaval, do organizations bear any social responsibility towards their employees when mandating workplace learning from their employees through e-learning? The International Organization for Standardization (ISO, 2007a) specifies four key areas that all organizations need to pay heed to for ‘social responsibility’ to be accomplished: “environment; human rights and labor practices; organizational governance and fair operating practices; and, consumer issues and community involvement/society development” (para. 6). Accordingly, given the criteria of “organizational governance and fair operating practices,” this article argues for e-learning adaptability as a burgeoning social responsibility in the workplace, when thinking about workplace learning, by discussing: (a) the workforce diversity, and other workplace changes, that increasingly challenge the current approaches to e-learning at work; and then, (b) highlights the e-learning adaptability framework (Remtulla, 2007) as one methodology to assess and enable e-learning adaptability to meet this social responsibility for the benefit of a global workforce.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".