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Record W2317039213 · doi:10.1061/40475(278)123

Fifty-One Names for Time

2000· article· en· W2317039213 on OpenAlexaboutno aff
Lee A. Peters, John L. Homer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsYesterdayDozenQuality (philosophy)SnowProduct (mathematics)Computer scienceResource (disambiguation)Process (computing)Value (mathematics)MeteorologyOperations researchHistoryBusinessGeographyEngineeringMathematics

Abstract

fetched live from OpenAlex

The Inuit language is said to have many precise names for snow — as many as fifty-one! Snow is a vital part of their existence. The Irish are said to have forty names for rain — and it rains a lot. Having many precise names helps communicate the current situation and the unique needs of that situation. The texture, moisture, shape, rate of fall, wind, and temperature are all known within that single name! Time to a project manager is in many ways similar to the snow of the Inuit or rain to the Irish. Time enters the project in different forms or shapes; its moisture and texture vary; and the rate of fall, the temperature, and the amount of wind all vary. The Project Management Business is a time management business where time is the only perishable resource in projects. All other resources do not disappear. They wait to be consumed another day. However, today is the only today that exists in project management. Today is the only day for working, for completing, for producing. Tomorrow is a different day with a different product. Other resources have time related values such as money — the value of money degrades with time but it does not disappear overnight. Time disappears S Today becomes Yesterday overnight! Let us say that again: `Time is the only perishable resource.' Besides disappearing, Time also has another unique quality — it changes during the project process. There may even be half a dozen different kinds of time within the same phase of the project. This is best explained by another metaphor. The project is quite similar to a white water river formed from the runoff of the snow melt. The project manager is the helmsman on a raft negotiating the river Understanding the time of the situation would help project managers lead the team and steer the progress past the rocks, eddies, holes, shallows and sand bars unique to that project. Many of these obstacles are there because of where the project is in its duration — its watershed. Knowing the kind of time helps the manager choose the right leadership behavior, the same leadership Will not be successful in all parts of the project river. Knowing the kind of time, allows the project manager to adjust vigilance, change the frequency of meetings, adjust the personal walk throughs, modify the report deadlines, review risks, identify opportunities.

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 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 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: Other · Consensus signal: Other
Teacher disagreement score0.893
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1210.009

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 head0.095
GPT teacher head0.371
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2000
Admission routes1
Has abstractyes

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