Pre-Registration Protocol v2.0: Comparative Dynamics of Knowledge Accumulation Networks
Abstract
This pre-registration protocol outlines a comparative network study evaluating the structural development and topological trajectories of heterogeneous knowledge-producing systems. Rather than evaluating static network snapshots, this study utilizes Dynamic Time Warping (DTW) and Principal Component Analysis (PCA) to quantify trajectory distances through a non-redundant, six-dimensional network-invariant state space across standardized temporal intervals. The primary hypothesis states that knowledge accumulation networks (e.g., scientific citation, legal citation, and software dependency networks) exhibit significantly greater structural trajectory correspondence with one another than expected under null models or when compared against non-knowledge control domains (e.g., biological networks). This protocol explicitly details standardized temporal normalization rules, class-specific missing data strategies, numeric effect-size floors, and falsification criteria designed to minimize post-hoc analytical flexibility and ensure open-science reproducibility.
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BibTeX
@misc{davidson2026preregistration,
title = {{Pre-Registration Protocol v2.0: Comparative Dynamics of Knowledge Accumulation Networks}},
author = {Craig Kyrle Strachan Davidson and Alfie Christian Strachan Davidson},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.6084/m9.figshare.34760289},
url = {https://zenodo.org/records/23605894},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Pre-Registration Protocol v2.0: Comparative Dynamics of Knowledge Accumulation Networks
AU - Craig Kyrle Strachan Davidson
AU - Alfie Christian Strachan Davidson
PY - 2026
DA - 2026/10/24
PB - Zenodo
DO - 10.6084/m9.figshare.34760289
UR - https://zenodo.org/records/23605894
AB - This pre-registration protocol outlines a comparative network study evaluating the structural development and topological trajectories of heterogeneous knowledge-producing systems. Rather than evaluating static network snapshots, this study utilizes Dynamic Time Warping (DTW) and Principal Component Analysis (PCA) to quantify trajectory distances through a non-redundant, six-dimensional network-invariant state space across standardized temporal intervals. The primary hypothesis states that knowledge accumulation networks (e.g., scientific citation, legal citation, and software dependency networks) exhibit significantly greater structural trajectory correspondence with one another than expected under null models or when compared against non-knowledge control domains (e.g., biological networks). This protocol explicitly details standardized temporal normalization rules, class-specific missing data strategies, numeric effect-size floors, and falsification criteria designed to minimize post-hoc analytical flexibility and ensure open-science reproducibility.
ER -