An Investigation of Social Media Use for Environmental Norm Development: An Exploration of an Emerging Multi-Actor Regulatory Governance Normative Process
Bibliographic record
Abstract
This dissertation posits that social media platforms (e.g., Twitter and Facebook) represent a new process and fora for the development and implementation of norms that regulate environmental behaviour, with unique characteristics, strengths, and weaknesses, when compared with conventional processes and fora for regulatory activity and codified norm development, such as state-based legislative assemblies and non-state standards bodies. Distinctive features of social media platforms affect user interactions and experiences, which in turn impact how environmental norm conversations take place on those platforms, as opposed to norm conversations that take place through legislatures or formal standards processes. In addition, distinctive characteristics of public sector, private sector, and civil society actors affect how they use social media in development of environmental norms. This research hypothesizes that the way in which social media is used to formulate environmental norms and the importance of social media as a venue for environmental norm conversations varies, depending in large part on distinctive characteristics of the governance actors (i.e., government, third sector actors, and the private sector) and their societal roles, interacting with the distinctive characteristics of social media. In addition to a cross-disciplinary review of scholarly literature, this dissertation explores the veracity of this hypothesis using two investigations; one that compares the use of Twitter by two Ontario government agencies, and a second case study that explores of a Twitter micronorm conversation about the groundwater operations of the water bottling industry in Ontario and other jurisdictions.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".