Twitter-Mediated Knowledge Brokering in STEM Education Reform: A Social Network Analysis of Key Knowledge Actors in a Mid-Sized Ontario School District During the First Year of Reform
Bibliographic record
Abstract
This thesis examines the influence of knowledge actors in a mid-sized Ontario school district's Twitter network, with a focus on STEM-related discourse during 2019 - the year of a major Ministry reform announcement. Using social network analysis (SNA), this research investigates how different types of actors - policy, research, and practice - facilitate knowledge exchange through three analytical lenses: (1) centrality measures to identify the most influential actors, (2) strength of ties to assess cohesion within and between subgroups, and (3) structural holes to explore how brokers bridge disconnected parts of the network. Centrality measures were used to determine the top ten ranked actors, highlighting who the main categories of knowledge actors were and what role they held inside or outside the district. The combination of centrality measures allowed for the exploration of the multifaceted ways in which they exerted influence within the district's Twitter network. Two knowledge actors, a STEM teacher and a science-focused school, with high outdegree and betweenness centrality played key roles in both disseminating STEM knowledge and brokering connections across otherwise disconnected groups. These findings highlight the multifaceted influence of certain individuals, such as teachers, schools, and consultants, who acted as both visible communicators and strategic connectors within the district's Twitter network. These findings indicate that the practice subgroup had the strongest ties, facilitating a higher volume of knowledge exchange within their group. In contrast, policy actors shared a significantly lower number of tweets or mentions amongst themselves, recording only eight interactions throughout the year. The strength of ties within the science subgroup facilitated more frequent knowledge exchange about science than in any other subject matter group during the first year of the reform. In contrast, the engineering subgroup had the weakest ties, sharing the least frequently, including only one exchange with the mathematics subgroup. The analysis of structural holes revealed that a small number of knowledge brokers played a key role in bridging otherwise disconnected clusters of knowledge actors about STEM, enabling information to span boundaries across the network. This thesis contributes to understanding online educational networks at a local level, providing insight into the influence dynamics within educational policy discourse on Twitter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".