Networks and the Development of the Irish Biotechnology \nSector
Bibliographic record
Abstract
Biotechnology, an umbrella term describing combinations of engineering and scientific \nknowledge from an array of disciplines used to produce products and processes from \nliving organisms, has been identified as a key sector for future economic developments \namong industrialised and industrialising nations as it blurs traditional boundaries \nbetween various industries. The Irish Government has introduced a series of initiatives \nto facilitate the development of an internationally competitive indigenous biotechnology \nsector since the late 1990s, yet no in-depth analysis of the sector relative to international \nsectoral characteristics, structures, or policy themes have informed their design or \nimplementation. This thesis analyses the Irish sector in the context of global sectoral \ndevelopments by studying the Post-Fordist organisational structure of the international \nsector, where biotechnology firms interact with various actors at different stages of the \nsectoral value chain in a variety of innovative networks determined by place specific \nactor and institution endowments that form local knowledge communities. Through \nqualitatively investigating the Irish sector's actors and collaborative network structure, \nthe thesis analyses the implications of the nature and character of these elements for the \nsector's future sustainability and development, and appraises existing Government \npolicies relating to sectoral developments. The thesis found that the on-going initiatives \nhave facilitated significant advances, yet have not addressed the legacy of pre-initiative \nresource and skill capacity weaknesses, while the sectoral value chain is fragmented as \nactors have developed poor networking arrangements due to their conservative natures, \nand the relative absence of key sector actors, skills and resources. These issues \ndemonstrate that a complex overarching policy framework is required so as to engender \nthe long-term development of a regionally tailored, systems-based support ecosystem \nwhich addresses existing structural weaknesses, and which facilitates and drives \nentrepreneurial and innovative activities throughout the sector's value chain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".