The local dimension of information spillovers: a critical review of empirical evidence in the case of innovation *. (Dialogue)
Bibliographic record
Abstract
Abstracts: Local Dimension of Information Spillovers: A Critical Review of Empirical Evidence in the Case of Innovation. The primary aim of this paper is to show that there is empirical evidence to support the theoretical claim of a link between information externalities and the agglomeration of information-intensive activities. Theoretical reasoning provides consistent and realistic explanations for the agglomeration of information-dependent activities including producer services and R&D. Empirical testing of these models raises a number of methodological problems. However, many empirical studies have been conducted into the local character of the diffusion of information. Much progress has been made over the last ten years and initial difficulties are progressively being overcome. Indirect tests provide a better understanding of information externalities and their consequences. These advances focus primarily on one category of information externalities: the information spillovers emitted and received by R&D. Most studies show a close link between the need for more or less tacit information exchanges and the effective proximity of agents and/or between this proximity and the production of innovation. ********** Au cours de ces dix dernieres annees, de nombreuses etudes ont ete effectuees sur le caractere localisd de la diffusion des informations, malgre l'existence de problemes methodologiques concernant la validation empirique de ces conjectures theoriques. Meme s'il n'existe pas de moyens directs d'evaluer les flux d'informations, des methodes indirectes permettent une comprehension plus precise des extemalit~s d'informations et de leurs consequences. La recherche empirique s'est concentree principalement sur une categorie particuliere d'externalites informationnelles, les [much less than] spillovers de connaissance [much greater than] lies aux activites de R&D et d'innovation. La plupart de ces etudes montrent l'existence d'un lien etroit entre besoin d'inforinations tacites et proximite et/ou entre proximite et production d'innovation. Knowledge traverses corridors and streets more easily than continents and oceans (Feldman 1994: 2). For the first time in history, it might be possible to locate on a mountain top and to maintain intimate, real-time, and realistic contact with business or other associates. All persons tapped into the global communication net would have ties approximating those used today in a given metropolitan region (Webber 1968 quoted in Moss 1987: 535). While Feldman considers that distance impedes the diffusion of knowledge, Webber claims that information interactions have become location-independent. These two contradictory statements have very different consequences in terms of the agglomeration of economic activities. Although it can be argued that thanks to new communication technologies, distance is now irrelevant to many information interactions, it is widely accepted that the more complex and strategic information interactions are still conditioned by close proximity or face to face contacts. The question is to determine which effect is prevalent. Basically, information exchanges generate spatial externalities. Indeed, information is, in some sense, a public good, because it is non-rival by nature: the individual who gives information still retains it. Information exchanges are therefore not really marketable and they generate a form of externality, i.e. non. price interaction or spillover. Moreover, other things being equal, distance is a barrier to the spread of information (Hagerstrand 1965), 50 that information flows generate spatial extemalities, that is non price spatial interactions (Fujita 1990), also called local spillovers. As far as information diffusion is spatially limited, information-intensive activities are expected to be located close to each other thereby maximising the benefits (Fujita and Ogawa 1982). …
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".