Instructor Competencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.375 | 0.035 |
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 it