From Concepts to Texts and Back: Operationalization as a Core Activity of Digital Humanities
Bibliographic record
Abstract
This article puts operationalization as a research practice and its theoretical consequences into focus. As all sciences as well as humanities areas use concepts to describe their realm of investigation, digital humanities projects are usually faced with the challenge of ‘bridging the gap’ from theoretical concepts (whose meaning(s) depend on a certain theory and which are used to describe expectations, hypothesis and results) to results derived from data. The process of developing methods to bridge this gap is called ‘operationalization’, and it is a common task for any kind of quantitative, formal, or digital analysis. Furthermore, operationalization choices have long-lasting consequences, as they (obviously) influence the results that can be achieved, and, in turn, the possibilities to interpret these results in terms of the original research question. However, even though this process is so important and so common, its theoretical consequences are rarely reflected. Because the concepts that are operationalized cannot be operationalized in isolation, operationalizing is not only an engineering or implementation challenge, but touches on the theoretical core of the research questions we work on, and the fields we work in. In this article, we first clarify the need to operationalize on selected, representative examples, situate the process within typical DH workflows, and highlight the consequences that operationalization decisions have. We will then argue that operationalization plays such a crucial role for the digital humanities that any kind of theory needs to take off from operationalization practices. Based on these assumptions, we will develop a first scheme of the constraints and necessities of such a theory and reflect their epistemic consequences.
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 | 0.046 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.007 | 0.100 |
| Scholarly communication | 0.025 | 0.061 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".