MétaCan
Menu
Back to cohort
Record W2487067563 · doi:10.22215/timreview/1002

Value Network Design for Innovations: Developing Alternative Value Network Drafts

2016· article· en· W2487067563 on OpenAlexvenueno aff
Martin Kage, Marvin Drewel, Jürgen Gausemeier, Marcel Schneider

Bibliographic record

VenueTechnology Innovation Management Review · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Value networkIntermediaryUpstream (networking)Industrial organizationDownstream (manufacturing)Value propositionValue captureBusinessComputer scienceMicroeconomicsValue creationEconomicsMarketingBusiness modelTelecommunications

Abstract

fetched live from OpenAlex

IntroductionThe well-known concepts of value or supply chains do not do justice to describing how value is created in today’s complex networks. Whereas traditional value chains have been characterized by strict upstream and downstream relationships, organizations nowadays interact as networked intermediaries (Porter, 1980; Yassine & Braha, 2003).

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)0.010
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.007
Science and technology studies0.0020.003
Scholarly communication0.0070.011
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0520.005

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 head0.051
GPT teacher head0.295
Teacher spread0.244 · 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 designTheoretical or conceptual
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueTechnology Innovation Management ReviewSame topicInnovation and Knowledge ManagementFrench-language works237,207