Bibliographic record
Abstract
Binary logic can be described as one response to humanity' s search for security from space and time.The primary means of this algorithmic 'solution' is through the reduction of technology (technē) to a 'techno-logic' -a logic of instrumentalized means and ends that transmits through culture in such a way that the indeterminacy of our participatory role in the world becomes encompassed by meta-narratives rather than involvement in the communicative space of culture.As I will reveal, these discursively-generated narratives persist through the thoroughly technologically-oriented spectacle that is Autistic Culture.Autistic Culture reveals genetic biases of agency toward auto-poiēsis, facilitated by instrumentalized technology.I will argue that Autistic Culture is a condition which can most effectively be mediated through architecture; through its role of establishing 'spontaneous' heuristic fictions as a basis for revealing moral ontologies. The intervention of a Laboratory for Human Prosthetics and Virtual Interfaces in theLower Main of Montréal (St.Laurent Boulevard), in the context of those at the forefront of technological development, to coincide with the development of the area into an arts and cultural district is seen to be most apt as an Architectural Therapy to negotiate Autistic Culture, and is manifested by means of a twofold process; by one which engages the 'local technology' of its inhabitants, as a means of developing a relationality; and by another which deliberately engages their autistic tendencies, as a means of contrasting ' extension' with quotidian experience.iii
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".