Bibliographic record
Abstract
In official statistics record linkage is an important activity, which consists in identifying records from the same individual in one or many files.It is used to combine data sources including admistrative, survey or big data sources.In practice, record linkage is subject to linkage errors when it relies on quasi-identifiers, such as names and demographic variables, which are nonunique and recorded with errors.Accounting for these errors is an important but challenging problem.In this work, two methods are described for the primary analysis of such data, i.e. an analysis by someone with unfettered access to all the related micro-data and project information.Both solu-B records are indexed over a subset B ⊂ {1, . . ., N }, where |B| is possibly different from |A|.We are interested in situations where the two files overlap significantly, i.e., where the ratio |A ∩ B| / min (|A|, |B|) is sufficiently large 1 .In the Cartesian product A * × B * , consider the pair (i, j) and define m ij and γ ij , the pair match status and comparison vector, respectively.Let M = [m ij ] 1≤i,j≤N denote the match matrix in A * × B * .In B * , let z j denote the observed responses from record j in B * , and define the vector z = z 1 . . .z N .As before, let X = x 1 . . .x N denote the matrix of all the covariates in register A * .Finally let y = y 1 . . .y N denote the actual responses, where y i is the actual response for record i in A * .As before, the finite population comprises of H IID blocks that each contain a variable but bounded number of IID individuals.Block h has size N h , where N h ≤ C for some constant C that does not depend on H, with N = N 1 + . . .+ N H .The block also corresponds to records indexed in the subsets A * h and B * h in the files A * and B * respectively, where |A * h | = |B * h | = N h .Let A h and B h denote the corresponding subsets in files A and B respectively, and let M h denote the match matrix in A * h × B * h ; the Cartesian product within the block. AssumptionsThe following assumptions are made that extend those of Section 3.3.A.1 The match matrix M h is a uniform random permutation independent of [x i ] i∈A * h .A.2 For i ∈ A * h , let j(i) denote the index of the corresponding record in B * h .The variables [y i ] i∈A * h , [I (j(i) ∈ B h )] i∈A * h , [I (i ∈ A h )] i∈A * h , M h , and {γ ij } (i,j)∈A * h ×B * h are conditionally mutually independent given the block size N h and the covariates [x i ] i∈A * h .1 When the overlap is small, statistical matching may be a better solution.where q ij is given by Eq. (4.1).Proof: Consider (i, j) ∈ A * h × B * h .As before, we have g (z j , x i ) =
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 | 0.052 | 0.150 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".