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Record W2898930378 · doi:10.1007/978-1-4842-3942-1_2

Unintended Consequences

2018· book-chapter· en· W2898930378 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

VenueApress eBooks · 2018
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategies and Innovation
Canadian institutionsInuit Tapiriit Kanatami
Fundersnot available
KeywordsMultitudeUnintended consequencesBusinessPublic relationsLaw and economicsPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Whether it is mismatched corporate processes, measuring the wrong things, or the lack of a consistent approach to help new business ideas succeed, there are a multitude of often undiscovered or undiscussed causes of failed corporate innovation—all of which can have dire consequences for your business. The wreckage that this failed innovation leaves behind goes beyond a few failed projects and can have ripple effects that negatively impact your entire company for years or even decades. Some of these impacts are tangible and obvious, but others are more hidden and insidious. Some of the most damaging consequences are from an intrapreneur and their team, acting alone, outside of any formal structure the larger company has in place. As this lone intrapreneur stays isolated and works to keep their new business off executive radars, they potentially cause issues beyond simply wasting money and other resources.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.628
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.224
Teacher spread0.176 · 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