Anthropocene U: Academic responses and responsibilities at Lakehead University
Bibliographic record
Abstract
This thesis explores the stories of six faculty members and administrators at \nLakehead University who are responding to the Anthropocene through their academic \nwork. Their stories suggest that there are barriers facing academic engagement with the \nAnthropocene and the associated possibilities for action are uniquely empowered by the \nparticular position and privileges of higher education; rich tensions arise in exploring the \nresponse-ability of the academy to the Anthropocene. I consider the planetary and \npedagogical contexts from which this research develops. Then, turning to participant \nstories, I look to appreciative inquiry, narrative inquiry, and place inquiry to guide my \ninteractions with their experiences in ways that intend to grow the community of scholars \nresponding to the Anthropocene at one Canadian university, Lakehead University in \nThunder Bay, Ontario. I next introduce the participants and the site of research through a \nseries of vignettes, and explore the experiences of participants as they work to respond to \nthe current moment on the planet. Their stories begin to illustrate the parallels between \nhow neoliberalism has helped usher in the Anthropocene and has shaped the university in \nways that minimize its ability to respond. The final chapter speaks to possibility and \npresents participants? visions for a University more responsive to the Anthropocene, \nillustrated by photographs of places that reflect participants? understandings of what is \npossible and that integrate place-voice into the research. This thesis concludes by \nsummarizing key themes, and by daring readers to consider their own response-abilities \nin the Anthropocene.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.067 | 0.027 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".