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Building Corporate Citizenship through Strategic Bridging in the Oil and Gas Industry in Latin America

2003· article· en· W2107761225 on OpenAlexaffabout
Percy Garcia, Harrie Vredenburg

Bibliographic record

VenueJournal of Corporate Citizenship · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBridging (networking)BusinessCitizenshipLatin AmericansCorporate social responsibilityPetroleum industryIndustrial organizationEconomic systemPolitical scienceEconomicsPublic relationsEngineeringLawPolitics

Abstract

fetched live from OpenAlex

Corporate citizenship is a concept that has been known and discussed by academics and practitioners for many years. Preston and Post (1975) refer to corporate citizenship as the final stage of socialisation of business organisations. This paper analyses the case of Pacalta Resources, a Canadian company which became involved in oil production in an environmentally and socially sensitive area of Ecuador, and proposes strategic bridging as a management strategy to build proactive corporate citizenship. The main findings of this research are: (a) the concept of corporate citizenship must be well understood by companies in terms of stages of socialisation (to engage in true collaboration and to benefit from it), (b) proactive corporate citizenship can be built by establishing strategic bridging and (c) the success of collaboration depends on the degree of independence and autonomy of the bridge organisation. ● Strategic bridging ● Corporate

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.111
GPT teacher head0.286
Teacher spread0.175 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations17
Published2003
Admission routes2
Has abstractyes

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