Bibliographic record
Abstract
I am enormously grateful to the readers of this journal for their kind attention to this work over the past 30 years. Conceptual analysis, the subject of my article, is primarily about clarifying meanings of those key concepts that are central to our collective work. Given the number of nebulous concepts in environmental education, and in education in general, this work has never ceased to be important — though, sadly, it is often neglected. Take, for example, a concept currently in vogue, social learning. Two ways of approaching this sometimes fuzzy concept would be, first, for authors to provide a clear articulation of the term, in their own view. What exactly does the idea of social learning involve for an author, and what are the implications for its use in the particular context in which it is used? This is a lot like clarifying one's own assumption about a concept for the benefit of the author and reader alike, and something we should be able to expect from all authors. Second, researchers can analyse the scope of a concept's usage within a body of literature, such as Rodela (2012) has done with social learning. This kind of analysis can provide a kind of heuristic for other researchers to navigate the extant usage of a key concept, and to point in future directions.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".