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Record W2114712648 · doi:10.1108/10878570010341663

Taking trouble:

2000· article· en· W2114712648 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

VenueStrategy and Leadership · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsWestern University
Fundersnot available
KeywordsHoly GrailProcess (computing)Position (finance)GlobalizationBusinessFocus (optics)Global strategyResource (disambiguation)MarketingEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Many corporations fail to find the Holy Grail of globalization because they have not paid “enough” ongoing attention to the process. Without greater attentional effectiveness in their efforts to globalize, firms waste precious executive resources or decide to standardize their operations to limit the complexity of their international strategies. Neither of these reactions is desirable. While companies can deploy a range of helpful tools in increasing overall levels of global attention, these tools are costly and not every company is in a position to achieve and sustain high levels of global attention effectively. In this article, the authors discuss three dimensions of management attention: aversion/attraction, captive/voluntary, and front‐of‐mind/back‐of‐mind. Each of these dimensions provides an array of tools to focus management attention. By maximizing each of these dimensions, attention effectiveness is increased. In an international business world with abundant information, managers need to focus on their most scarce resource – management attention.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.159
GPT teacher head0.272
Teacher spread0.113 · 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