Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
By their nature, digital games facilitate surveillance. They allow for the compilation of statistics, internal states, and rules to be recorded, thus hiding many of the internal workings from the players and making the games much more complex. This digitization makes it much easier to collect player data and metrics, and then, as a process of function creep, to use this data in new and innovative ways, such as improving the user experience, or subtly shaping users' in-game desires and behaviours. Increasingly, these practices have moved from non-game spaces into social networking sites and spaces of play.The "gamification" movement is benefiting from the increasing sophistication of such metrics. Gamification combines the playful design and feedback mechanisms from games with users' social profiles (e.g. Facebook, twitter, and LinkedIn) in non-game applications explicitly geared to drive behavioural change (e.g. weight loss, workplace productivity, educational tools, and consumer loyalty). As critics point out, gamified applications rely on the points, leaderboards, and badges often seen in games, but are not games in themselves (Deterding 2010; Bogost 2011). Advocates of the gamification movement - including Al Gore in a recent Games for Change keynote - argue that this monitoring and feedback makes difficult tasks more playful and enjoyable (McGonigal 2011; Gore 2011). However, the marketing and political discourse of using games to change behaviour in positive ways is quite different from messy actualities rooted in advertising, consumption, and intrusive user monitoring. The current potentials to ‘gamify’ life have incited debate on whether the spread of these points based systems heralds playful utopias or dystopic surveillant societies run by corporations and advertisers. This paper highlights the rise of gamification and the implications for surveillance studies. In particular, it focuses on describing the increasingly intrusive monitoring practices are propagated under the banner of fun and play.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it