Looking Back With Logos the Cat: Unsettling the Gaze in Multispecies Research
Bibliographic record
Abstract
In this article, we reflect on the preparatory phase of a multispecies research project focused on dog–human care relationships. Playing with creative posthumanist methodologies that seek to decenter the human, we attached light-weight videorecording devices to our companion animals’ collars. As we approach a dogs-eye view of our everyday lives and interactions, we think with Jacques Derrida to ask: What does it mean to respond when met with the animal’s gaze? Through unsettling our gaze, the videos take us somewhere else entirely, raising another question: What do attempts to tangibly and imaginatively see with the dog do ? The unsettling of our own reflections—on the question of the animal and the gaze of the “Other”—offers a space for enacting Haraway’s conception of response-ability, for moving with our impetus to respond despite our current situated involvement in neoliberalism and settler colonialism with their commodification and domestication of more-than-human life.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".