Bibliographic record
Abstract
In this paper I present a narrative approach to environmental education research. This approach evolved through a dynamic interplay between research questions, theory, experience, conversation, and reflection. I situate the approach with respect to narrative inquiry and clarify the key conceptual metaphors underpinning my study, including “story,” “narrative,” and “metaphor.” I then discuss the particular methods involved and their compatibility with my underlying ontological and epistemological assumptions about the storied reality of human experience. Finally, I reflect on writing as part of the research process and on the possibilities and responsibilities inherent in the act of writing. Resume Dans cet article, je propose une approche narrative de la recherche en ERE. L’evolution de cette approche s’est faite a la faveur d’une vive interaction entre elements de recherche, theorie, experience, conversation et reflexion. Je situe cette approche quant a son rapport avec le questionnement narratif et precise les principales metaphores conceptuelles sur lesquelles repose mon etude, soit le « recit », la « narration » et la « metaphore ». J’aborde ensuite chacune des methodes touchees et leur compatibilite avec mes hypotheses ontologiques et epistemologiques sur la realite, mise en recit, de l’experience humaine. Enfin, je reflechis sur l’ecriture en tant que composante du processus de recherche ainsi que sur les possibilites et les responsabilites inherentes a l’acte d’ecrire.
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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".