‘One Day…': <i>Google</i> 's Project Glass, integral reality and predictive advertising
Bibliographic record
Abstract
In the spring of 2012, Google unveiled ‘Project Glass' with the promise to deliver an augmented reality head-mounted display device to the masses. Its YouTube video ‘One Day…', viewed more than 20 million times, promotes a utopian vision of what life will be like once this prototype becomes available to everyone. Using a critical humanities approach, this paper uses this event as a site to consider the implications of predictive advertising as a rising cultural phenomenon within the public imagination. Predictive advertising can be described as a new hybrid form of communication, which capitalizes both on the enigmatic appeal of futuristic techno-fantasies and the realism of the advertisement as a reference to an external fact. This rhetorical manoeuvre resembles Jean Baudrillard's assertions that culture has fallen victim to ‘integral reality' whereby real and imaginary are no longer distinct. We pinpoint this transformation within this age of network culture and mass adoption of new mobile and wearable devices, suggesting that ‘announcing the future' is an activity that rightfully deserves a genre of its own under the label predictive advertising.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".