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Record W1956956323

Technology offsets: structuring a new strategy for industrial benefits

2014· article· en· W1956956323 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Technology Management · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsStructuringCoproductionNegotiationOrder (exchange)BusinessPrincipal (computer security)Industrial organizationTechnology strategyCompetitive advantageMarketingStrategic managementPublic relationsPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

The changing structure of reciprocal trade practices or industrial benefits has received increased attention over the past five years by managers of advanced technology companies. In particular, technology offsets are being used by newly industrialized countries to forge a bold new trade strategy in order to become major players in this costly competitive game. The consequence of these compensatory arrangements has placed increased emphasis on developing strong linkages through licences, coproduction, turnkeys and marketing know–how. The principal focus of this paper is to examine technology offsets, a form of industrial benefits, in light of the dynamics of negotiating contracts from the international manager's perspective. In addition, an agenda for technology offset undertaking is examined in which influencing variables are separated in order to understand the competitive behaviour of the principal actors. The authors have researched and interviewed 25 high–technology companies which provide excellent case studies to support the theme of the paper.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
GPT teacher head0.255
Teacher spread0.149 · 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