Start-ups, Stagnancy, and Story-telling Strategies: Western Business News Writing on Japanese entrepreneurialism
Bibliographic record
Abstract
This paper reports research into the written reporting on Japanese entrepreneurialism in western, English-language business news. After explaining the author’s method of sampling from a six-year survey of major-circulation newspapers and business magazines, the project’s main methodology is described – a qualitative form of rhetorical research known as Critical Discourse Analysis (CDA), focused on uncovering and critiquing the ideological biases and presuppositions, both explicit and implicit, in texts such as business news stories. The paper’s following analytic section then applies many of the explained CDA concepts to close discursive analysis of one news article on Japanese entrepreneurialism, randomly selected from a larger set of studied sources, with comparative insights drawn from two other news articles also randomly selected for close analysis. This illuminates a variety of journalistic shortcomings common in western business journalism on Japan, mostly involving an understated or even surreptitious authorial effort to frame reported issues and events as though the neoliberal demand for Japan’s political-economic westernization – specifically the aspiration towards a ‘Silicon Valley’ model of entrepreneurialism – were the only rational interpretation for readers to adopt. Primary examples include: the attempt to ‘prime’ audience interpretation of reported phenomena and events by exaggerated or false declaratives, in titles or body text, followed by qualifying or corrected statements; an overstated attribution of centrality and causality to the dramatized actions of elite or sympathetic individuals, thereby obscuring broader socio-cultural and political-economic factors and contexts; the use of loaded and euphemistic terms to subtly slant reader comprehension in favour of the author’s overt or underlying propositions; and implied support for criminal or discriminatory behaviour and discourse. The paper’s concluding discussion issues a call for less ideologically and ethnocentrically biased reportage in western business journalism on Japan, whatever the ideology advanced by writers and publishers, and for more variety of perspective and debate.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".