How Chet Bowers’ Writings Contribute to A/Moral Vulnerability for EcoJustice
Bibliographic record
Abstract
Chet—thank you for responding to my rejoinder. Since you bring it up and lest we leave readers in the dark about the main theme of the conference paper you are referring to (presented in Windsor, Canada at the 2008[a] Provoking Research ProVoking Communities Conference), I provide a brief referent. The main theme of that paper was to discuss why setting up a scarecrow argument around E.O. Wilson, Richard Dawkins, and Carl Sagan as spokesmen for science creates an easy target for those who want to scrutinize science for its shortcomings based on these scientists. For those who are unaware, a scarecrow is another word for straw man argument, where the theorist sets up a misrepresentation of say, science, or quotes a person’s words out of context, or presents a scholar’s work unfairly—all to refute a position and make it look like anyone who holds the position has been defeated. While Bowers is right in critiquing these scientists for the ways in which they misrepresent the sciences, they do not speak for science and are not taken seriously by many knowledgeable educators worldwide. Science, itself, cannot be critiqued on this basis, however. Doing so is ethically irresponsible to the sciences (for more, see Mueller, 2009a/b, Mueller & Bentley, 2009; Mueller & Tippins, 2009; Tippins et al., 2010). Moreover, the paper mentioned addresses how Bowers deemphasizes or ignores the socioscientific trends rapidly emerging in science and environmental education, which specifically focus on ecojustice concerns. But it is very difficult to know what is going on inside schools if one spends very little time in the classroom, which is an assumption based on the silences of the particular, inadvertently perpetuated in Bowers’ work. Nonetheless, it does not seem ethical to discuss a person’s working ideas presented at a conference only to use the nonexistent description of these ideas as a way to make a position look like it has been defeated, so that those who side with the more relational approach to ecojustice that I argue will also appear defeated.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".