Other Ways to Skin a Cat: The Social Identity Jobs-to-be-Done Theory as it Applies to Independent Magazines Surviving Technological Disruption
Bibliographic record
Abstract
This cross-disciplinary study of print magazines facing technological disruption asks: Why do we like print magazines?Why are they important to society?What business strategies appear to be working in protecting publications from technological disruption?From a review of technological disruption to magazines since their inception, this study finds that magazines that survived earlier disruptive periods did so through content innovation.However, content is not enough to protect against the most recent technological disruption caused by the Internet and mobile devices.Instead, a combined theory is proposed, called the Social Identity Jobs-to-bedone (SIJ) theory, purporting that we seek print magazines for their role defining our social identities.From an analysis of four case studies of independent magazines at the niche, city, national and international level that are surviving technological disruption, it is argued that the SIJ theory can help publishers determine what areas of their business to protect and expand.I am pleased to have the opportunity to thank the many colleagues, friends and faculty members who have helped me with this research project.I am most indebted to Dr. Christopher Waddell, the supervisor of this thesis, for sharing his research expertise and wisdom in connection with this project.I am also appreciative of his constant willingness to offer helpful answers, advice and quick replies to my many questions.Equally, I
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.054 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".