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Conclusion and Future Directions

2014· book-chapter· en· W2492440873 on OpenAlexaff
Kimiz Dalkir, Susan McIntyre

Bibliographic record

VenueAdvances in human resources management and organizational development book series · 2014
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsDefence Research and Development CanadaMcGill University
Fundersnot available
KeywordsKnowledge managementCorporate governanceCritical success factorAction (physics)Organizational cultureResource (disambiguation)BusinessProcess managementManagement sciencePolitical scienceComputer scienceEngineeringPublic relations

Abstract

fetched live from OpenAlex

The contributors to this collection have identified the critical success factors, obstacles, and future opportunities for ensuring that lessons learned processes contribute to the attainment of organizational goals. The critical success factors are categorized as: 1) conducive culture, 2) effective leadership, 3) robust lessons learned cycle, and 4) action plans. Obstacles to success are: 1) cultural barriers, 2) resource limitations, 3) lack of governance, and 4) insufficient analysis for credible results. Future directions to explore include the roles of culture and leadership, the need for standardized approaches that can be used by all types of organizations, improved technological infrastructure, and implementation of effective measurement systems. Opportunities for future research are: determining why organizations either choose to or cannot learn; the role of trust in learning lessons; optimization of technological approaches; and how lessons learned approaches could integrate processes and procedures from other organizational improvement.

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)0.014
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0040.004
Scholarly communication0.0120.017
Open science0.0050.005
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0720.018

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2014
Admission routes1
Has abstractyes

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