Interpreting Apple’s visions: Examining the spatiality of the Apple Vision Pro
Bibliographic record
Abstract
This paper extends previous empirical and theoretical research by applying a critical analysis to the recent discourse of “spatial computing,” a term used by Apple Inc. in the launch of the Apple Vision Pro headset. We focus primarily on advertisements and other official videos used to promote and communicate the value(s) of Apple's inaugural “spatial computer,” and how this spatiality is mediated through the hardware and software of the Apple Vision Pro. With techno-utopian visions of daily life that belie Apple's broader platform politics, we argue that Apple's promotional content reveals sociocultural, embodied, material, spatial, and other imaginaries of extended reality that present Apple's novel mediations and data capture as normal and desirable for users who are framed as affluent, able-bodied tech enthusiasts participating in a digital economy that blurs work and leisure.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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 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".