Boston Metropolitan Area Biotechnology cluster/La Grappe Biotechnologique De la Region Metropolitaine De Boston
Bibliographic record
Abstract
The biotechnology industry has developed in a number of tight spatial clusters in the US and in other countries. This paper examines the biotechnology cluster in the Boston metropolitan area. Preliminary investigation indicates that firms in this cluster are specialized in technologies related to medical sciences and that they are highly concentrated in one small part of the metropolitan area: the City of Cambridge. In order to better understand the forces that underlie this type of tight clustering, a survey of biotechnology firms was conducted as well as a number of face to face interviews. Results indicate the importance of local universities both as sources of skilled labour and as producers of technological advances with commercial application. L'industrie de la biotechnologie s'est developpee aux etats-Unis et dans d'autres pays sous forme de grappes bien delimitee spatialement. Cet article examine la grappe biotechnologique dans la region metropolitaine de Boston. Des recherches preliminaires indiquent que les entreprises dans ce secteur sont specialisees dans les technologies reliees aux sciences medicales et qu'elles sont fortement concentrees dans une pdrtie de la region metropolitaine : la ville de Cambridge. Afin de mieux comprendre les forces qui sous-tendent ce type de grappe, une enquete aupres des entreprises en biotechnologie a ete conduite de meme qu'un nombre d'entrevues en personne. Les resultats indiquent l'importance des universites locales comme sources de main d'oeuvre specialisee et comme productrices de technologies ayant un potentiel commercial. Introduction Biotechnology is one of the fastest growing industries in the developed world. It is also one of the most spatially clustered industries in the world. In the U.S., clusters in San Diego, the San Francisco Bay area and the Boston metropolitan area account for a disproportionately high share of total employment and investment. In this paper, we examine the Boston metropolitan area cluster. We show that there is an exceptionally high degree of clustering within this regional cluster--specifically in the City of Cambridge, Massachusetts. We may regard Cambridge as an industrial district in the tradition of Marshall (1890). Subsequent research has shown that there are a variety of mechanisms that give rise to industrial districts. In order to shed some light on the underlying mechanisms, we present the results of a survey of biotech firms located in the Cambridge area and in other parts of the Boston metropolitan area. The results help us eliminate some of the standard explanations for tight agglomeration and identify others that clearly play important roles. We also suggest some factors that have not been much discussed in the literature to date and that relate to the peculiarities of the biotechnology industry. The remainder of the paper is organized as follows. First, there is an overview of theoretical and empirical work on industrial districts. This is followed by an introduction to the biotechnology industry in general and to the biotechnology industry in Massachusetts in particular. We then present evidence of the extraordinary clustering of Boston Metropolitan Area firms in the City of Cambridge. Following a description of our survey instrument, we consider a number of possible explanations for the observed tight clustering. For each explanation we present evidence from our own survey and from other related research. We conclude by identifying those underlying mechanisms that best explain the clustering and suggesting directions for further research. Industrial Districts An industrial district may be defined broadly as a group of related industries located in the same region (Held 1996). Industries may be 'related' in a couple of ways. In the case of the classic industrial complex, industries are related by the transfer of intermediate goods among them. In such cases, agglomeration economies arise due to decreased transportation and transactions costs. …
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".