Instructor Competencies
Bibliographic record
Abstract
This edition is not just a rehash of old, albeit classic and still important, stuff. Instead, it provides a fresh perspective on a topic of perennial interest for those working in the field that has been variously called training and development, human resource development, performance technology, and workplace learning and performance. The fresh perspective takes into consideration two additional instructor settings to the traditional face-to-face environments that most instructors and trainers know -- that is, online and blended settings. These settings are, of course, becoming more critical as instruction moves beyond classroom settings to include virtual and combinations of classroom and other media delivery methods.The ibstpi instructor competencies match up well to Mapping the Future (Bernthal, Colteryahn, Davis, Naughton, Rothwell, & Wellins 2004), the current ASTD competency study of the field now known as Workplace Learning and Performance (WLP) and previously known as Training and Development (T&D). WLP is more than a new name for an old subject and represents a fundamental paradigm shift in what it means to be a professional in the field formerly known as training. WLP is all about getting improved performance -- and therefore improved results -- in organizational settings through planned and unplanned learning interventions. Instruction is thus a means to an end and not an end in itself. The ibstpi instructor competencies dovetail well with that philosophy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.046 |
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".