Netflix or news? An examination of young Canadians’ appetite to pay for online journalism
Bibliographic record
Abstract
This thesis explores factors that influence the willingness to pay (WTP) of young Canadian adults for digital journalistic/news content using the uses and gratifications (U&G) approach. U&G is a user-centred theory of examining how people use media to satisfy needs and desires. Using semi-structured interviews with 13 participants in the 18 to 34 age cohort as a data collection method, it emerged that the willingness to pay for online journalism/news content is currently low, although some participants are open to paying for online news that they would consider to be unique, or of high enough quality. Those who were not open to paying point to the non-exclusivity of online news as the chief reason. Participants appeared more willing to pay for non-journalism/news digital media, such as Netflix. Findings suggest that charging money for online journalism in this age cohort in Canada will be challenging given the multiplicity of media choices they have.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".