Accessing primary sources for marketing and advertising history research from family history websites
Bibliographic record
Abstract
Purpose The purpose of this study is to respond to the Journal of Historical Research in Marketing special issue call for discussions that can assist advertising and marketing history researchers locate primary sources of interest to their research by describing the resources available through the online family history websites Ancestry.com and FindMyPast.com. Design/methodology/approach Brief histories of Ancestry and FindMyPast are presented, based on publicly available records and secondary sources. This paper explains the types of data researchers can access via Ancestry.com and FindMypast.com, the costs of access and then provides some examples of how these resources have been used in past research by marketing and advertising historians. Findings Family history websites such as Ancestry and FindMyPast can provide researchers with access to a wide variety of data sources, such as census and voting records; immigration records; city directories; birth, marriage and death records; military records; and almanacs and gazetteers, but at a cost. In some cases, paying for digital access to records is more convenient, timely and can cost less than travelling to access these same documents in physical form. Depending on the researcher’s geographical location and the country from which records are sought, this can add up to quite a cost savings. When using these sources, it is wise to determine which database contains more of the records you are searching for; Ancestry tends to have better US and Canadian resources, while FindMyPast covers the UK better. Originality/value Researchers interested in conducting advertising and marketing history research need access to primary data sources. Given restricted travel budgets and, indeed, restricted travel under COVID-19 conditions, gaining access to primary sources in digital form can allow researchers to continue their work. At any time, gaining access to digital records without having to travel can speed up the research process. Researchers new to the field, and those with many years of experience, can benefit from learning more about family history databases as primary data sources.
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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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.133 | 0.040 |
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".