Overcoming Variance and Process Distinctions in Information Systems Research
Bibliographic record
Abstract
Most Information Systems research to date has been conducted from either a variance or a process perspective. On the one hand, some researchers argue that process and variance approaches should be kept separate, while others think they should be combined. In this paper, we argue that variance and process have more similarities than expected, and that combining them can have significant advantages for the field. We propose a ‘blended’ hybrid approach in which elements of both variance and process exist in a coherent whole, and offer a first-version tool box to start hybrid theorizing. Our paper ends with a discussion regarding the types of information systems research questions that can be appropriately approached from a hybrid standpoint.
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 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 | 0.095 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.021 | 0.044 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".