Bibliographic record
Abstract
The case of the 2010 municipal elections in Calgary, Canada, is used here to explore the discursive construction of social media in relation to political engagement. This article examines the way in which 59 undergraduate students at the University of Calgary discuss political engagement through Facebook and Twitter. Participants enthusiastically constructed a vision of ‘engagement’ fostered by social media’s alleged intrinsic features. Social media, it was argued, create a feeling of community, provide access to information as well as the ability to share it, and open up new means of building personal connections between politicians and citizens. In this articulation, social media appeared as both the tool that produced engagement and the space where this engagement unfolded. The focus of the article is on questioning the implications of this discursive construction by asking what political possibilities are opened up or closed down in this articulation? The construction of social media as the solution to the problems of democracy remains highly problematic, yet also indicative of a deep preoccupation with the conditions of modern life, and particularly the desire to find solutions to the increased complexity of the social systems.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.040 | 0.071 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".