Addressing Deficits: How Crowdfunded Journalists Find Success \nin a Restructuring Media Industry
Bibliographic record
Abstract
This thesis examines crowdfunded journalists’ beliefs about the most effective ways to appeal to potential financial supporters and successfully meet their funding goals. In the wake of continually worsening economic restructuring in the journalism industry and the disruption of journalism’s perennial advertising-based business model (Anderson, Shirky, & Bell, 2014; Kaye & Quinn, 2010), crowdfunding has emerged over the last decade as a possible alternative business model (Hunter, 2016). The financial crisis has resulted in cutbacks in coverage and staff, and an erosion of journalism’s societal role as the fourth estate (Gasher et al., 2016; McChesney & Pickard, 2011; Mensing, 2007; Picard, 2014; Public Policy Forum, 2017). \n\tThe existing literature on what motivates people to support crowdfunded journalism has looked at the question primarily from supporters’ point of view (Aitamurto, 2011; Jian & Shin. 2015). However, there has been little research on what crowdfunded journalists themselves think are the best ways to motivate or appeal to potential financial supporters. Using in-depth, semi-structured interviews (as per Castillo-Montoya, 2016 and Leech, 2002) with 10 Canadian journalists who have engaged in crowdfunding for new media outlets, this thesis examines key value propositions the journalists use to motivate their audiences, the general obstacles they face when convincing their audience to pay, as well as promotional techniques and other related practices that contribute to successfully reaching their funding goals. The interviews were analyzed thematically (as per Guest, MacQueen, & Namey, 2012) using a constant comparative method and open coding to track emerging themes. The results show that respondents are crafting value propositions by alluding to deficits created by industry restructuring, and the negative impact this restructuring has on democratic society.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".