Telling our stories on the web: Canadian English-language web series and the production of culture online
Bibliographic record
Abstract
This dissertation presents the first critical scholarly analysis of the Canadian English-language scripted web series industry, its cultural practices, industrial dynamics and texts. Through in-depth interviews with 48 individuals active in the production of Canadian online scripted content, participant observation, and a benchmark quantitative analysis of gender and race in key creative roles in 175 seasons of Canadian web series, the dissertation investigates the web as an alternative space for Canadian scripted audiovisual content, and the actors and forces that have shaped and are shaping its development, including its emergent patterns of inclusion. By developing a novel theoretical framework that combines the critical political economy of communication with entrepreneurship studies, the dissertation is able to mediate effectively between structure and agency to reveal how Canadian web series creators are interpreting, internalizing and resisting larger institutional dynamics and discourses in their cultural practices and texts. Through their entrepreneuring, Canadian web creators are reacting to a variety of rigidities within the contextual dimensions in which they are embedded, including the absence of meaningful opportunities to practice their crafts, the persistence of networks of exclusion, and inaccurate or missing on-screen representations of themselves or others in mainstream media. Through their work, they desire to achieve freedom from these constraints. The challenge of disrupting the status quo is then revealed through an examination of the domestic and extra-national structural factors that act as impediments to their agency. The dissertation problematizes ideas of participation and access on the web, and introduces new conceptual terminology through the Participatory Culture Paradox, to encapsulate the contradictory set of relations that on the one hand, enables creators’ activities in the online space, and at the same time, constrains their capacity to find audiences and monetize their work. The findings here demonstrate that as much as internet-based distribution has expanded opportunities for participation for regular users, who you are, and where you are based, continue to be salient mediators of both participation and success in the development of professional scripted screen careers in the digital age. The dissertation culminates in actionable priorities for Canadian policy that aim at change.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.031 | 0.019 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".