Being Brave: Writing Environmental Education Research Texts
Bibliographic record
Abstract
The heroine came back from her very important quest and sat down to write a thesis ... While mythical journeys do not always end this way, the stories have to be told. The work of telling the story in the hero’s journey is often left untold. This paper explores some of the headwork that goes into textwork (Van Manen, 1995) in environmental education research. We argue that writing is an integral part of the research process, and should not viewed as an ‘add on’ or a silent, untold part of the adventure. We reflect on some of the institutional and epistemological issues associated with writing social science (in our case environmental education) research texts. Writing research is never an easy enterprise, it is bound by history and tradition, convention, institutional habit and regulation. It is also constrained by the uncertainty of the process of writing itself, by problems of power relations in research, and the difficulty of writing to represent experience rigorously and authentically while recognizing that all writing is a constructed symbolic representation of experience. The paper reflexively reviews our attempts at ‘being brave’ in the construction of our research texts.
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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.049 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".