Researching Memory Work and Identity Work
Bibliographic record
Abstract
Memory work (i.e. ‘how we (re)construct the past’) and identity work (‘who we think we are’) are deeply intertwined with collective remembering shaping and being shaped by the construction of individual and collective identities. This symposium seeks to advance organizational memory studies and explore the linkages of memory work to identity work as both of these forms of social symbolic work are important for understanding how members respond to the multi-faceted, concurrent challenges that organizations face. It aims to develop scholarship by exploring important new directions stemming from: 1) the opportunity to examine how memory work enriches the study of identity work (and vice versa); 2) the opportunity to explore memory work and identity work at different levels of analysis and their effects; and 3) the opportunity to explore the potential value of focusing on temporality for theorizing on memory work and identity work. In this symposium, we want to stimulate discussion about this connection and how scholars can better reimagine the organization from the inside out, i.e. the purposeful reflexive efforts that its members engage in when constructing and negotiating memories and identity, by unlocking a wave of insights from bringing together the study of remembering and being.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".