Bibliographic record
Abstract
This generative, theoretical descriptive paper, presents a framework I have designed to help teachers create on-line lessons that weave science from the curriculum into mythology. By mythologizing the curriculum, the teacher broadens the mindset of their students by allowing them to see the living-Earth as interconnected. This framework is timely, as many students are reacting to the policy around climate change, or absence of it, with fear and anxiety. The culminating artistic project of the digital game provides an opportunity for expression. It calls on the player to create a work of art that connects what they have learned from the game with their own experiences. The artistic project values the perspective of each contributor so that their anxieties can be heard. A direction for future research is a study that is embedded in a workshop for teachers. The workshop gives teachers an opportunity to learn about the framework while the study aims to learn about the myth-building experiences of teachers. The study follows the arts-based-research paradigm so that primacy can be given to the myths created by the teachers. As an exemplar of this framework, I present an on-line game I have created that connects several domains pertinent to the education of climate change including the personal, the social, and the scientific. The Google Sites game includes an assessment with instructions.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".