Running With and Like my Dog: An Animate Curriculum for Living Life Beyond the Track
Bibliographic record
Abstract
More than a playful inquiry, questioning what is it like to run 'with' and 'like' a dog provides a philosophical and tangible point of entry for re-exploring notions of a 'lived', or rather, a 'living' curriculum. Dogs have extreme perception, yet due to traditional hierarchical distinctions, human-animal intertwinings of consciousness are rarely explored laterally or with reversibility. Drawing upon Merleau-Ponty's common element of 'flesh' and Deleuze's notion of molecular becomings, this inquiry delves into life beyond the rigidity of our culturally constructed, forward-facing comportment. So often we humans run through life with self-imposed blinders. We run with a view fixed on the horizon, a gaze that is not open to the possibilities of the path we have the potential to not only follow, but to create. Dogs, by contrast, experience the world phenomenologically as they perceive it for what it really is: a slew of sentient wonder. As we approach what it might be like to be more like our dogs in the way we run through and shape our course in life, an animate curriculum for running off and beyond the linearity of a self-imposed track, tenure, athletic or otherwise, awaits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".