Table Scraps: An Actionable Framework for Multi-Table Data Wrangling\n From An Artifact Study of Computational Journalism
Bibliographic record
Abstract
For the many journalists who use data and computation to report the news,\ndata wrangling is an integral part of their work.Despite an abundance of\nliterature on data wrangling in the context of enterprise data analysis, little\nis known about the specific operations, processes, and pain points journalists\nencounter while performing this tedious, time-consuming task. To better\nunderstand the needs of this user group, we conduct a technical observation\nstudy of 50 public repositories of data and analysis code authored by 33\nprofessional journalists at 26 news organizations. We develop two detailed and\ncross-cutting taxonomies of data wrangling in computational journalism, for\nactions and for processes. We observe the extensive use of multiple tables, a\nnotable gap in previous wrangling analyses. We develop a concise, actionable\nframework for general multi-table data wrangling that includes wrangling\noperations documented in our taxonomy that are without clear parallels in other\nwork. This framework, the first to incorporate tablesas first-class objects,\nwill support future interactive wrangling tools for both computational\njournalism and general-purpose use. We assess the generative and descriptive\npower of our framework through discussion of its relationship to our set of\ntaxonomies.\n
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".