Jobs-to-Be-Done and Journalism Innovation: Making News More Responsive to Community Needs
Bibliographic record
Abstract
Developing successful innovations in journalism, whether to improve the quality and reach of news or to strengthen business models, remains an elusive problem. The challenge is an existential concern for many news enterprises, particularly for smaller news outlets with limited resources. By and large, media innovation has been driven by never-ending pivots in the search for a killer solution, rather than by long-term strategic thinking. This article argues for a fresh approach to innovation built around the “jobs to be done” (JTBD) hypothesis developed by the late Clayton Christensen and typically used in business studies of innovation. However, attempts to bring the JTBD framework into the news industry have never taken hold, while scholars, too, have largely overlooked the framework in their study of journalism innovation. We argue that the JTBD approach can foster local journalism that is more responsive and relevant to the needs of local communities. It reorients journalism by focusing on identifying and addressing the underserved needs of communities, as understood by the communities themselves. It suggests that a bottom-up approach to appreciating the “jobs” that community members want done offers a model that supports both the editorial and business imperatives of local news organizations.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| 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".