Bibliographic record
Abstract
This article enacts our ongoing collaborative experiments utilizing “iMessaging” on iPhone as a practice of critical relationality toward building our Indigenous-settler millennial academic friendship. Holding written text alongside our iMessage conversations, we confront three threads that continually interject in our exchanges: (1) what happens with our fleshy bodies when we connect with iMessage; (2) how our co-created, but uncommon, iMessage-body exchanges are an experiment with potential modes of Indigenous-settler academic friendship; (3) and how our iMessaging practice makes real the academic futures that we hope, and need, to contribute to. Together, we grapple with how the iMessaged space we create in our friendship might enable us to be attentive to the disjunctures between Indigenous knowledges and feminist science studies. We wonder how we might think of iMessage as a mode of friendship that is potentially capable of challenging settler-colonial normativities and temporalities of academic relating, while also calling us to attend to the complexities of our bodied lifeworlds as we iMessage our (digital) flesh, futurities, and friendship as young, emerging scholars.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".