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Autopoietic Approach for Information System Development

2008· book-chapter· en· W4256155584 on OpenAlexaff
El‐Sayed Abou‐Zeid

Bibliographic record

VenueIGI Global eBooks · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsConcordia University
Fundersnot available
KeywordsAutopoiesisKnowledge managementInformation systemComputer scienceContext (archaeology)Management scienceEpistemologyCognitive scienceArtificial intelligenceEngineeringPsychology

Abstract

fetched live from OpenAlex

In the last decade a new generation of information systems (IS), such as enterprise resource planning, Web-based information systems and knowledge management support systems, have emerged in response to ever-changing organizational needs. As these systems are socio-technical phenomena in which social and technical factors interweave the ways in which people work, the issue of “how to integrate the work activity and social context of users into the IS which is being designed” becomes one of the principal problems of IS development (Bai et al., 1999). Therefore, the need for new information system design theories is recognized. According to Walls et al. (1992), an “IS design theory” must have two aspects—one dealing with the description of the system and one dealing with the prescription, that is, the process of developing of the system. The prescription aspect includes a description of procedures and guidelines for system development. In addition, these two aspects have to be grounded on theories from natural or social sciences, that is, kernel theories. Therefore, the development of new IS design theories requires a closer look at the system theories that go beyond the traditional system theory that is based, among other things, on Cartesian dualism, that is, mind/body or cognition/action, and on a model of cognition as the processing of representational information (Mingers, 2001). One of the candidate theories is the theory of autopoiesis, which can be best viewed as a system-grounded way of thinking with biological foundations, together with its extension into social domain.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.937

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.278
Teacher spread0.246 · 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 teacher head, 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

Citations0
Published2008
Admission routes1
Has abstractyes

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