Returning ‘learning’ to education: Toward an ecological conception of learning and teaching
Bibliographic record
Abstract
This article describes a notion of learning as adaptive semiotic-growth. In line with the theme of this special issue, learning will be approached on a broad ecological and evolutionary continuum – most generally expressed as a form of adaptation to the environment. Viewing learning through the criterion of signification (semiosis) means that learning is continuous across the entire biological realm. Both the life process and the learning process are expressed through forms of semiotic-engagement and involve continual adaptation and meaning-making. Thus, learning cannot be seen as unique to humans. Learning is more broadly ecological before it is “cultural”. From here we can imagine educational institutions as forms of exaptation, that evolved naturally to channel learning more effectively. Thinking of learning on an ecological continuum means that learning cannot be “located” or pinned down easily in educational research or practice. Rather, learning has a sporadic identity; it is emergent in the specificity of events and must be discerned within the practices that enact it. Realizing learning as something emergently enacted in the educative encounter, and not something that can be determined and implemented, allows us to resist turning learning into an accountability tool that can easily be used towards ideological ends.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".