Bibliographic record
Abstract
This paper explores ways of motivating persons of good will to see the signs of a planet in distress, recognize our human and political paralysis, and change our behavior in ways commensurate to the historical challenge of our time. Framing this exploration within a “tapestry of stories”, the author examines ways creation itself learns, for clues as to how humans may grasp the large systems changes and collapses in which we are involved and/or implicated. Thomas Berry calls this the “great work” of our time. In the end, the paper concludes that the power we need to address climate change is not education, activism or financing, as important as these are. As an integral dimension of a 13.8 billion year evolutionary process, humans’ reflexive consciousness, is a power we can call upon. We need a major shift in consciousness, related to a new understanding of our place in the universe.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".