Bibliographic record
Abstract
"In the field of Communication studies, mobile technology is still a relatively new area of study with scholarly research just beginning to address this rapidly growing field. Because this technology is continually evolving, as is the way in which people are starting to use it, much of the research remains inconclusive as to any predictions of how this technology will to be used. In the meantime, much has been made about mobile technology's potential to change the way we interact and communicate with one another, and how these changes might have the ability to alter social relations forever. What I would like to examine are the locations where mobile technology and social relations intersect, and the manner in which the two inform each other. More specifically, I would like to focus on the areas where mobile technologies (cell phones, Blackberries, text messaging) affect social life, such as collective behaviour, political action, as well as public sphere and public space issues. Much has been made about the supposed benefits of technology and its potential to collectivize, politicize and, above all, mobilize our society. However, is this constant telephony really living up to this potential? In an environment already saturated with communication technology, billion-dollar advertising expenditures and media multinationals, will the addition of new technologies benefit those seldom heard or only add to the white noise?"--Pages 2-3.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".