Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The deleterious effects of anthropogenic climate change have prompted scholars across disciplines to critically rethink knowledge production, cultural memory, and shared legacies. TERA—a transnational group of seven scholars and artists affiliated with McGill University (Canada), with members in the Caribbean, the UK, and the US—translates this imperative into experimental archiving. This auto-ethnographic article details our collaborative, digital archive project focused on technology, ecology, and religion in North America, framing archiving as a process perpetually under construction. Archive-making and taxonomic ordering were technologies of European conquest in North America. Acknowledging our enmeshment in coloniality, TERA adopts “serious parody” (Wilcox 2018) of nineteenth-century naturalist clubs: spaces where educated, wealthy, white men curated plundered or “discovered” objects. We subvert these power structures through collective methods, emphasizing taxonomy’s socioecological implications. Formed in 2021, TERA convenes monthly online to curate artifacts (visual/auditory art, poems, infographics, performances) for a 2023 digital archive. Recorded discussions and analyzed transcriptions reveal collaborative knowledge-making processes obscured by univocal taxonomies. Our work confronts questions like “what does it mean to be human in a shared biocultural life- world?”, reimagining human-nonhuman relationships and strategies for ecological crisis.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it