The Phoneur: Mobile Commerce and the Digital Pedagogies of the Wireless Web
Bibliographic record
Abstract
Corporate data collectors track every move you make on the electronic landscape, recording your m-commerce phone habits only to sell them back to you through advertising that entices you to spend more. Think of these phones as a kind of remote-controlled radio collar—like the ones scientists use to monitor the behaviour of the animals they study. The move toward mobile commerce, or m-commerce, still nascent in North America, is an attempt to create a worldwide datastructure built on the premise of consumption. The habit@ is the electronic environment—and the sociocultural conditions—of the wireless and worldwide webs (W4 and W3). Within it, “The structures constitutive of a particular type of environment … produce habitus , systems of durable, transposable dispositions, structured structures predisposed to function as structuring structures” (Bourdieu, 1977, p. 72; 1990). These structures are the invisible data collection mechanisms that track user habits through the data flows of electronic identity formation, and sell these habits back to the user in the form of “push” advertising. 2 “There is no fixed self, only the habit of looking for one” (Wise, 2000, p. 303), and this habit of looking is our habit@on-line. The wireless habit@ is constituted in the patterns of our mobile browsing behavior, which is in turn repackaged and re-presented as a demographic representation of how we will engage the spending process, how we will actualize ourselves as desiring-machines (Deleuze and Guattari, 1983). The habit@ is becoming the marker of social distinction. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".