MétaCan
Menu
← Back to cohort
Record W4366493942 · doi:10.46692/9781447337751.002

The man who invented a chicken: Introducing a global generation of entrepreneurial social activists

2018· other· en· W4366493942 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsSociology

Abstract

fetched live from OpenAlex

Begin with an individual, and before you know it you find that you have created a type: begin with a type, and you find you have created – nothing. (F. Scott Fitzgerald, The Rich Boy ) Social entrepreneurs are not content just to give a fish or to teach people how to fish; they will not rest until they’ve revolutionised the whole fishing industry. (Bill Drayton, founder, Ashoka Foundation for Social Entrepreneurs) A journey into the Indian village Vinod Kapur was working at a Swedish company based in India that specialised in the making and selling of matches when he decided to leave it all behind and dedicate his life to rearing chickens. He had considered this for a while, for he had a brother and he thought that chicken farming might be a way of helping him into some kind of trade. But one day in 1963 Kapur was told he had a shadow on his lung, and with a second child on the way he wondered if it might be the case that chickens were for him too. He heard about new chicken varieties imported from Canada that could survive and withstand tough Indian conditions. He could start small, grow incrementally. When I met him in India in 2017, he was sporting a comfortable cardigan and a tidy mop of white hair. He was 82 by then, seated behind a large desk. He leaned forward as he reminisced on this and said to me: “This, young man, is how destiny works.” The plan at first was to import chickens for rearing in India. However, the government of the day didn’t much like the idea of creating an Indian industry that was dependent on foreign imports. So Kapur and his associates had a second idea. From an American supplier he obtained a quantity of germplasm , pure breeding stock that could give rise to new generations of healthy birds. This made the idea self-sufficient enough for the bureaucrats. Over a few years, supported by loans from his family, from these seeds he would build India’s first genetic poultry breeding business. It did well. In the early 1970s, he moved his head office to some land just off a major highway in the growing city of Gurgaon, to the south west of India’s capital, New Delhi.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0210.014
Scholarly communication0.0150.015
Open science0.0020.021
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0120.003

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.

Opus teacher head0.024
GPT teacher head0.252
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicEntrepreneurship Studies and Influences→French-language works237,207→