Inside the Black Box of Dictionary Building for Text Analytics: A Design Science Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this paper is to develop and demonstrate a dictionary building process model for text analytics projects following the design science methodology. Using inductive consensus-building, we examined prior research to develop an initial process model. The model is subsequently demonstrated and validated by using data to develop an environmental sustainability dictionary for the IT industry. To our knowledge, this is an initial attempt to provide a normalized dictionary building process for text analytics projects. The resulting process model can provide a road map for researchers who want to use automated approaches to text analysis but are currently prevented by the lack of applicable domain dictionaries. Having a normalized design process model will assist researchers by legitimizing their work requiring dictionary building and help academic reviewers by providing an evaluation framework. The resulting environmental sustainability dictionary for IT industry can be used as a starting point for future research on Green IT and sustainability management.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it