Bibliographic record
Abstract
On behalf of both the editorial board and the readership of the Journal of Time Series Analysis, I would like to take this opportunity to thank Professor Peter Robinson for his many years of dedicated service to our journal. Peter is stepping down as a Co-Editor with effect from 1st January 2018. I am, however, delighted to announce that Peter has agreed to remain on the editorial board as an Advisory Editor, with effect from 1st January 2018. Peter was first appointed to the editorial board in 1994. I have also greatly benefitted from his wise counsel since I took over as Editor of the journal and would like to personally thank Peter for all the help, advice and support he has given me. Morten Nielsen of Queen's University Canada. Morten holds the David Chadwick Smith Chair in Economics at Queen's and holds a Canada Research Chair in Time Series Econometrics. Morten's interests are wide-ranging but principally include: estimation and testing in fractional integration and co-integration models; semiparametric analysis of long memory processes; financial econometrics and high frequency data, and unit root and co-integration testing. Suhasini Subba Rao is Professor in the Statistics Department at Texas A&M University. Suhasini has published on a wide range of topics within time series analysis including: non-stationary and nonlinear processes, estimation and inference of time-varying ARCH processes, spatio-temporal processes, random coefficient models, nonparametric estimation for dependent data, and stationarity testing. Finally, while the Journal of Time Series has over the course of its history from time-to-time published shorter communications style papers we have tended to receive relatively few submissions of this type. We have therefore decided to explicitly develop a new section of the journal, titled Notes and Comments, dedicated to shorter communications papers and also articles which comment on papers which have been published in the journal. The remit of this new section, as laid out in our instructions to authors, is as follows: “Notes and Comments Section. This section of the journal publishes note-length communication papers. This is intended to facilitate the rapid dissemination of novel research ideas. Comments following up on previously published papers in the journal can also be considered for publication this section. The subject matter of all contributions to this section should lie within the standard aims and scope of the journal. All potential contributions will be peer-reviewed but that process will be expedited to facilitate rapid dissemination. Submissions should normally be no longer than 10 pages in total. On-line supporting material can be used in conjunction with articles published in this section.”
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.180 | 0.157 |
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".