Seventy years of information systems development methodologies from early business computing to the Agile era: A two-part history Part 2: Later ISD to Early post ISD methodology era: Adapting to accelerated context expansion (1980–today)
Bibliographic record
Abstract
Information systems design (ISD) methodologies emerged soon after business computers in the 1950s. They have been a central topic of research and professional discourse in the information systems (IS) field ever since. This is Part 2 of a two-part history of ISD methodologies from the pre-methodology era that laid the foundational thinking that has been incorporated into ISD methodologies until now. We apply a historical method to follow the narrative of ISD methodology evolution in a historical context to identify central innovations and milestones that changed the environment allowing new types of ISD outcomes and processes to emerge demanding novel methodological responses. We will study what changed, what stayed the same and where the major shifts occurred. Part 2 reports on the major innovations and milestones that changed the IS environment and lead to emergence of the Later ISD (1980–1990) and Early post ISD methodology era practices (1990–today) and associated methodological innovations and principles. In Part 1, we have reported on the Pre ISD (1880–1960) and the Early ISD methodology era (1960–1980) histories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".