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Excellent Systems Analysts

2000· book-chapter· en· W2501145742 on OpenAlexaff
M. Gordon Hunter

Bibliographic record

VenueIGI Global eBooks · 2000
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsSystems development life cycleSoftware developmentComputer scienceSoftwareOperations managementSoftware development processWork (physics)Software maintenanceOperations researchBusinessRisk analysis (engineering)Process managementEngineeringOperating system

Abstract

fetched live from OpenAlex

There is evidence which suggests the software crisis still exists and is negatively impacting both information systems (IS) development and maintenance. Kendall (1992) has reported IS development backlogs averaging 30 work-months. Others (Senn, 1985; Yourdon, 1989) including Kendall (1992) suggest a hidden backlog, users’ plans not even submitted as requests because of the identified backlog, may result in IS development delays of up to four to seven years. Further Laudon and Laudon (1998) have determined that 51 percent of software development projects require up to three times more than the initial budget for both cost and time. The situation regarding IS maintenance is also of concern. Kendall (1992) suggests the IS maintenance software crisis has resulted from problems created in phases prior to programming. This situation is further confounded by the fact that the later in the System Development Life Cycle (SDLC) that an error is discovered, the more it costs to fix (Boehm, 1981).

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.180
Threshold uncertainty score0.602

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1800.106

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 head0.050
GPT teacher head0.261
Teacher spread0.211 · 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 designQualitative
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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