Extreme freshwater events, scientific realities, curriculum inclusions, and perpetuation of cultural beliefs
Bibliographic record
Abstract
The purpose of this research was to explore and open dialogue about possible connections between the scientific realities of extreme freshwater events (EFWE), a lack of EFWE-related curricular content in schools, and future teachers’ awareness and perceptions of EFWE. In phase one, an analysis of existing weather data demonstrated ongoing moderate to severe EFWE in the two regions under investigation, Queensland, Australia and Saskatchewan, Canada, at the time of data collection. In phase two, a content analysis of school curricula in the two regions shows a dearth of mandatory content related to EFWE, though Queensland, Australia had slightly more mandated content than did Saskatchewan, Canada. In phase 3, a survey of pre-service teachers in the two regions showed a demonstrable lack of recognition of undergoing moderate to severe EFWE at time of data collection, along with a general satisfaction with the current level of curricular coverage of the topic. While respondents’ overall concern was low, there were consistent regional differences. Queenslanders were more likely to recognize their lived experience with EFWE and perceived it to be a more important inclusion in school curricula than their Saskatchewanian counterparts. Taken together, results suggested that learned cultural truths were reflected in and perpetuated by school curricula. Results highlighted cultural denial of EFWE severity and a need to change false truths by increasing visibility of EFWE in mandated school curricula. The authors propose that results warrant further research and discussion as it relates to public policy and prioritizing EFWE in formal school curricula.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".