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Record W7024244928

Reflecting on Vancouver’s Current Youth Media Funding Trends in the Context of Neoliberalism

2016· dissertation· en· W7024244928 on OpenAlexaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsNeoliberalism (international relations)Context (archaeology)Face (sociological concept)Government (linguistics)Work (physics)InterviewYouth participationYouth studies
DOInot available

Abstract

fetched live from OpenAlex

Youth media organizations and programs in Vancouver provide diverse opportunities for youth.However, my thesis argues that neoliberalism and discourses about the information society and creative industries have shaped youth media funding since the 1990s.Through interviewing youth media representatives in Vancouver, my findings indicate that these funding trends create a number of challenges for youth media organizations and programs.Organizations face precarious funding, have to rely on unpaid labour, and are confronting competitive funding environments that can impact how organizations collaborate.In addition, funding is becoming increasingly narrow and focused on individual skills development, which stands in contrast to the diverse work associated with youth media organizations and programs.Given the challenges that organizations face within current funding trends, I conclude that there is a need for sustainable government funding models for youth media programs in Vancouver.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.200
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0120.003
Scholarly communication0.0090.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.072
GPT teacher head0.340
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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