On the Methodological and Philosophical Challenges of Sociomaterial Theorizing: An Overview of Competing Conceptualizations : ICIS 2012 Panel Statement
Bibliographic record
Abstract
This panel discusses how to take the ontological paradigm of Sociomateriality to the field using alternative theoretical lenses that embody sociomaterial ideas. Based on exemplary papers, Samer Faraj, Karlheinz Kautz, Daniel Robey, and Ulrike Schultze present the advantages of the lens they have drawn on to inform their empirical research. Through their discussion the panelists illustrate how they designed their studies accordingly and defend why their approach allows them to make empirical observations of the Sociomaterial. Informed by this comparative debate, the audience gains insights into the panelists’ experiences with conducting, writing, and editing Sociomaterial research. The audience will thus understand the methodological differences of the alternative lenses as well as important commonalities that make a study sociomaterial. As a key takeaway, the panel provides guidance on how to contribute to sociomaterial theorizing, thus supporting the recent trend towards Sociomateriality in the IS research community.
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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 | 0.040 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".