Data Quality in Secondary Data Analysis: A Case Study of Ecological Data using a Semiotic-based Approach
Bibliographic record
Abstract
Data quality problems are widespread in secondary data when they are used for data warehousing and data mining. This paper advocates a broad semiotic approach to data quality. The main premises of this expanded semiotic framework are (1) data represent some reality, (2) data are created and interpreted by humans in a communication process, (3) data are used for specific purposes by humans, and (4) data cannot be created, interpreted and used without knowledge. Thus, the semiotic-based approach to data quality in secondary data analysis has four aspects: (1) representational, (3) communicational, (3) pragmatic, and (4) knowledge-based. To illustrate these four characteristics, we present a case study of ecological data analysis used in the creation of an ornithological data warehouse. We discuss the temporal data (ecological notion of time), spatial ecological data (communication processes and protocols used for data collection), and bioacoustic data processing (domain knowledge needed for the specification of data provenance).
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 | 0.070 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".