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EFFECTIVE PERFORMANCE MANAGEMENT

2009· article· en· W2021475161 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

VenueJournal of Business Logistics · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsAgile software developmentAnalogyComputer scienceProsperityPoint (geometry)Plan (archaeology)Variation (astronomy)Consistency (knowledge bases)BusinessProcess managementRisk analysis (engineering)MarketingOperations managementEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

The synergistic effect of holistically addressing all of the variables highlighted by this article will result in organizations consistently reaching their intended destination. Skipping steps creates the illusion of speed, but rarely results in progress. While effective performance management is incredibly difficult, it is also critical to an organization's survival and prosperity. Systematically addressing these critical success factors will ensure consistency and success: The starting point—A clear, objective understanding of current reality, as it is . The destination—A clear point of view on where you want the organization to be, taking the current realities into account. The path—A growth plan that will take you from the current reality to the intended destination. Variation—A culture and system that expects variation, distinguishes between noise and signal, ignores the noise, and acts on the signals. Agile Course Correction—A strong foundation that increases the number of course correction opportunities dramatically. Alignment—Ensuring that everyone works towards the same destination. The article uses a flight analogy to explain each of these critical variables.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.020
GPT teacher head0.237
Teacher spread0.216 · 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