Bibliographic record
Abstract
Purpose This study aims to explore, illuminate and hence evoke further reflections on the implications of creating and conserving distinctions that inherently act as simplifications and limit appropriate action. Design/methodology/approach The approach used was reflective regarding the chosen concept of designing and learning from the perspective of a constitutive epistemology. These were investigated as circularities and as distinctions in language. The variety of intended meanings and hence implicit entailments was examined from the perspective of implicit domains. Findings A tendency to focus on the results of designing and learning rather than the processes was attributed to several factors including cultural relevance, tangibility, durability and observability. Further, it was found that result and process are arbitrary distinctions in a circular system. It was noted that lack of awareness of multiple domains encourages reification, and that distinctions inherently obscure what happens in the non-articulated aspects of living. However, expertise embraces an ability to attend to such “betweens”. This applies to expertise in the assessment of learning and designing. Originality/value The most obvious value of the findings is for the field of education. The insights gained indicate that the path of individualized learning with an emphasis on attention to the processes, inclusive of those that are not distinguished and named but can, with reflective experience, be sensed and acted on, has deep epistemological roots. A further implication is that educators require expertise to effectively work with learners, and that effective assessment depends on recurrent conversational interactions between the educator and learner.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".