A Brave New Internet: Hacking the Narrative of Mark Zuckerberg’s 2021 Introduction of the Metaverse
Bibliographic record
Abstract
We are entering an era of “techlash”: increasing unease with the hold of large technology companies over our lives, driving by fatalistic feelings of loss of agency. Neither attempts by these companies to address such concerns, such as appointing ethical committees and ombudsmen, nor grassroot initiatives aimed at user empowerment, seem effective in addressing this. This context remains unacknowledged in Mark Zuckerberg’s introduction of the metaverse on 28 October 2021. We will show, however, that it is still implicitly addressed through its narrative. A far reaching transformation of the way in which we use the internet ispresented as desirable and unescapable, employing an epic narrative mode which values constancy of the individual and their mastery over their surroundings. However, this future is shaped by Zuckerberg and his company; promising agency for all, it is remarkable how little agency is given to the user. We juxtapose this smooth future vision with a counternarrative using the same narrative building stones, but told in a narrative mode distributing agency more equally. Thus, we engage in strategic analysis, exploring how to resist narratives such as themetaverse’s. We call this method “hacking the narrative.”
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".