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Record W2086147520 · doi:10.1177/0162243912473163

Pushes and Pulls

2013· article· en· W2086147520 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Technology & Human Values · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSupply and demandTechnological changePerspective (graphical)EconomicsNeoclassical economicsIndustrial organizationMicroeconomicsComputer scienceMacroeconomics

Abstract

fetched live from OpenAlex

Much has been written about the linear model of innovation. While it may have been the dominant model used to explain technological innovation for decades, alternatives did exist. One such alternative—generally discussed as being the exact opposite of the linear model—is the demand-pull model. Beginning in the 1960s, people from different disciplines started looking at technological innovation from a demand rather than a supply perspective. The theory was that technological innovation is stimulated by market demand rather than by scientific discoveries. However, few traces of the demand-pull model remain in the literature today. This article looks at what happened to the demand-pull model from a historical perspective, at three points in time: birth, crystallization, and death. It suggests that the idea of demand as a factor explaining technological innovation emerged in the 1960s, was formalized into models in the 1970-1980s, then got integrated into “multidimensional” models. From then on, the demand-pull model disappeared from the literature, existing only as an object of the past, like the linear model of innovation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.016
GPT teacher head0.249
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it