Bibliographic record
Abstract
This thesis explores the value of social media in contemporary democratic practices; more precisely, on the use of social media in Canadian tar-sands pipeline infrastructure debate through the lens of public sphere theory. The study aims to contribute to improved understanding of Twitter’s shaping the course of the proposed Energy East pipeline, its legitimacy and formation of public debate around it. It is based on a mixed-methods approach employing both qualitative and quantitative research methodology. Data was collected from a topic-specific content stream on Twitter, followed by a series of semi-structured interviews with some of the most influential users within a sample of collected tweets. The study identified the users, the content and socio-political context of tweets that are posted in connection with the pipeline as well as users’ perceptions of Twitter as a tool for online deliberative democratic practices. \nFindings indicate Twitter is praised for offering an enabling environment for citizen journalism on real-time events, its swiftness of information dissemination, enabling contact with individuals outside of users’ established social circles and the power to influence public opinion. However, the medium is not without limitations which diminish its role as an optimal tool for democratic online public deliberation. My study suggests the main hindrance for this is the absence of constructive debate due to Twitter’s character-limitation of posts and predominantly one-sided communicative processes that take place within this medium. Its role in Energy East debate remains constrained within informative and reactive aspects of its service on current developments on the pipeline polemics and has as such a limited influence on legitimation processes surrounding the project. I therefore conclude that Twitter represents only a fragment of what can be considered the new public sphere and definitely not one-size-fits-all solution to the contemporary legitimation crisis of proposed large-scale industrial projects such as Energy East pipeline.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".